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overflow: hidden; text-overflow: ellipsis; -webkit-line-clamp: 3; -webkit-box-orient: vertical; }</style><div class="col-xs-12 clearfix"><div class="u-floatLeft"><h1 class="PageHeader-title u-m0x u-fs30">Probability and statistics</h1><div class="u-tcGrayDark">84,593 Followers</div><div class="u-tcGrayDark u-mt2x">Recent papers in <b>Probability and statistics</b></div></div></div></div></div></div><div class="TabbedNavigation"><div class="container"><div class="row"><div class="col-xs-12 clearfix"><ul class="nav u-m0x u-p0x list-inline u-displayFlex"><li class="active"><a href="https://www.academia.edu/Documents/in/Probability_and_statistics">Top Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Probability_and_statistics/MostCited">Most Cited Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Probability_and_statistics/MostDownloaded">Most Downloaded Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Probability_and_statistics/MostRecent">Newest Papers</a></li><li><a class="" href="https://www.academia.edu/People/Probability_and_statistics">People</a></li></ul></div><style type="text/css">ul.nav{flex-direction:row}@media(max-width: 567px){ul.nav{flex-direction:column}.TabbedNavigation li{max-width:100%}.TabbedNavigation li.active{background-color:var(--background-grey, #dddde2)}.TabbedNavigation li.active:before,.TabbedNavigation li.active:after{display:none}}</style></div></div></div><div class="container"><div class="row"><div class="col-xs-12"><div class="u-displayFlex"><div class="u-flexGrow1"><div class="works"><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_43178927" data-work_id="43178927" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/43178927/STATISTICAL_LECTURES_By_Dr_Saad_Mahmood_Ali">STATISTICAL LECTURES By: Dr. Saad Mahmood Ali</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Statistics: is the science of collection, organization, presentation, analysis, and reasonable interpretation of data. It also deals with methods and techniques that can be used to draw conclusions about the characteristics of a large... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_43178927" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Statistics: is the science of collection, organization, presentation, analysis, and reasonable interpretation of data. It also deals with methods and techniques that can be used to draw conclusions about the characteristics of a large number of data points--commonly called a population by using a smaller subset of the entire data.<br />Statistics is sometimes described as the science of decision making under uncertainty and can be divided into two broad areas as follows:<br />Descriptive Statistics Quantities and techniques used to describe a sample characteristic e.g. mean, standard deviation, box-plot. <br />Inferential Statistics which covers those statistical procedures used to help draw conclusions or inferences about a population on the basis of a sample of data collected from the population. Important areas inferential statistics include confidence intervals, hypothesis tests, regression analysis and experimental design. Underlying inferential statistics is the idea of probability and probability distributions.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/43178927" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7224d09e15712f3891053b633125d5fa" rel="nofollow" data-download="{"attachment_id":63444051,"asset_id":43178927,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/63444051/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="4118673" href="https://itswtech.academia.edu/SaadMahmoodAli">Saad M A H M O O D Ali</a><script data-card-contents-for-user="4118673" type="text/json">{"id":4118673,"first_name":"Saad","last_name":"Ali","domain_name":"itswtech","page_name":"SaadMahmoodAli","display_name":"Saad M A H M O O D Ali","profile_url":"https://itswtech.academia.edu/SaadMahmoodAli?f_ri=22613","photo":"https://0.academia-photos.com/4118673/1594469/33693748/s65_saad.ali.jpg"}</script></span></span></li><li class="js-paper-rank-work_43178927 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="43178927"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 43178927, container: ".js-paper-rank-work_43178927", }); 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$(".js-view-count[data-work-id=43178927]").text(description); $(".js-view-count-work_43178927").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_43178927").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="43178927"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a>, <script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="85362" href="https://www.academia.edu/Documents/in/Descriptive_Statistics">Descriptive Statistics</a>, <script data-card-contents-for-ri="85362" type="text/json">{"id":85362,"name":"Descriptive Statistics","url":"https://www.academia.edu/Documents/in/Descriptive_Statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="177212" href="https://www.academia.edu/Documents/in/Interval_analysis">Interval analysis</a>, <script data-card-contents-for-ri="177212" type="text/json">{"id":177212,"name":"Interval analysis","url":"https://www.academia.edu/Documents/in/Interval_analysis?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="201265" href="https://www.academia.edu/Documents/in/Trial">Trial</a><script data-card-contents-for-ri="201265" type="text/json">{"id":201265,"name":"Trial","url":"https://www.academia.edu/Documents/in/Trial?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=43178927]'), work: {"id":43178927,"title":"STATISTICAL LECTURES By: Dr. Saad Mahmood Ali","created_at":"2020-05-27T08:17:11.633-07:00","url":"https://www.academia.edu/43178927/STATISTICAL_LECTURES_By_Dr_Saad_Mahmood_Ali?f_ri=22613","dom_id":"work_43178927","summary":"Statistics: is the science of collection, organization, presentation, analysis, and reasonable interpretation of data. It also deals with methods and techniques that can be used to draw conclusions about the characteristics of a large number of data points--commonly called a population by using a smaller subset of the entire data.\nStatistics is sometimes described as the science of decision making under uncertainty and can be divided into two broad areas as follows:\nDescriptive Statistics Quantities and techniques used to describe a sample characteristic e.g. mean, standard deviation, box-plot. \nInferential Statistics which covers those statistical procedures used to help draw conclusions or inferences about a population on the basis of a sample of data collected from the population. Important areas inferential statistics include confidence intervals, hypothesis tests, regression analysis and experimental design. Underlying inferential statistics is the idea of probability and probability distributions.\n","downloadable_attachments":[{"id":63444051,"asset_id":43178927,"asset_type":"Work","always_allow_download":false},{"id":63443458,"asset_id":43178927,"asset_type":"Work","always_allow_download":false},{"id":63444054,"asset_id":43178927,"asset_type":"Work","always_allow_download":false},{"id":63448760,"asset_id":43178927,"asset_type":"Work","always_allow_download":false},{"id":63448761,"asset_id":43178927,"asset_type":"Work","always_allow_download":false},{"id":63443686,"asset_id":43178927,"asset_type":"Work","always_allow_download":false},{"id":63444020,"asset_id":43178927,"asset_type":"Work","always_allow_download":false},{"id":63443993,"asset_id":43178927,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":4118673,"first_name":"Saad","last_name":"Ali","domain_name":"itswtech","page_name":"SaadMahmoodAli","display_name":"Saad M A H M O O D Ali","profile_url":"https://itswtech.academia.edu/SaadMahmoodAli?f_ri=22613","photo":"https://0.academia-photos.com/4118673/1594469/33693748/s65_saad.ali.jpg"}],"research_interests":[{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false},{"id":85362,"name":"Descriptive Statistics","url":"https://www.academia.edu/Documents/in/Descriptive_Statistics?f_ri=22613","nofollow":false},{"id":177212,"name":"Interval analysis","url":"https://www.academia.edu/Documents/in/Interval_analysis?f_ri=22613","nofollow":false},{"id":201265,"name":"Trial","url":"https://www.academia.edu/Documents/in/Trial?f_ri=22613","nofollow":false},{"id":223715,"name":"Sample","url":"https://www.academia.edu/Documents/in/Sample?f_ri=22613"},{"id":747665,"name":"Statistics Inferential","url":"https://www.academia.edu/Documents/in/Statistics_Inferential?f_ri=22613"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_4141060" data-work_id="4141060" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/4141060/Accounting_for_Multivariate_Probabilities_of_Failure_in_Vertical_Seawall_Reliability_Assessments">Accounting for Multivariate Probabilities of Failure in Vertical Seawall Reliability Assessments</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/4141060" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0a23bbb16dcdbbc5768a16183ff1bd79" rel="nofollow" data-download="{"attachment_id":31651386,"asset_id":4141060,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/31651386/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="6875" href="https://cicese.academia.edu/VanesaMagar">Vanesa Magar</a><script data-card-contents-for-user="6875" type="text/json">{"id":6875,"first_name":"Vanesa","last_name":"Magar","domain_name":"cicese","page_name":"VanesaMagar","display_name":"Vanesa Magar","profile_url":"https://cicese.academia.edu/VanesaMagar?f_ri=22613","photo":"https://0.academia-photos.com/6875/79691/87523/s65_vanesa.magar.jpg"}</script></span></span></li><li class="js-paper-rank-work_4141060 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="4141060"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 4141060, container: ".js-paper-rank-work_4141060", }); 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$(".js-view-count[data-work-id=4141060]").text(description); $(".js-view-count-work_4141060").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_4141060").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="4141060"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="3100" href="https://www.academia.edu/Documents/in/Flood_Risk_Management">Flood Risk Management</a>, <script data-card-contents-for-ri="3100" type="text/json">{"id":3100,"name":"Flood Risk Management","url":"https://www.academia.edu/Documents/in/Flood_Risk_Management?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a>, <script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="74657" href="https://www.academia.edu/Documents/in/Flood_risk_assessment_and_management">Flood risk assessment and management</a>, <script data-card-contents-for-ri="74657" type="text/json">{"id":74657,"name":"Flood risk assessment and management","url":"https://www.academia.edu/Documents/in/Flood_risk_assessment_and_management?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="410234" href="https://www.academia.edu/Documents/in/Climate_change_adaptation_measures">Climate change adaptation measures</a><script data-card-contents-for-ri="410234" type="text/json">{"id":410234,"name":"Climate change adaptation measures","url":"https://www.academia.edu/Documents/in/Climate_change_adaptation_measures?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=4141060]'), work: {"id":4141060,"title":"Accounting for Multivariate Probabilities of Failure in Vertical Seawall Reliability Assessments","created_at":"2013-07-30T17:22:04.751-07:00","url":"https://www.academia.edu/4141060/Accounting_for_Multivariate_Probabilities_of_Failure_in_Vertical_Seawall_Reliability_Assessments?f_ri=22613","dom_id":"work_4141060","summary":null,"downloadable_attachments":[{"id":31651386,"asset_id":4141060,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":6875,"first_name":"Vanesa","last_name":"Magar","domain_name":"cicese","page_name":"VanesaMagar","display_name":"Vanesa Magar","profile_url":"https://cicese.academia.edu/VanesaMagar?f_ri=22613","photo":"https://0.academia-photos.com/6875/79691/87523/s65_vanesa.magar.jpg"}],"research_interests":[{"id":3100,"name":"Flood Risk Management","url":"https://www.academia.edu/Documents/in/Flood_Risk_Management?f_ri=22613","nofollow":false},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false},{"id":74657,"name":"Flood risk assessment and management","url":"https://www.academia.edu/Documents/in/Flood_risk_assessment_and_management?f_ri=22613","nofollow":false},{"id":410234,"name":"Climate change adaptation measures","url":"https://www.academia.edu/Documents/in/Climate_change_adaptation_measures?f_ri=22613","nofollow":false},{"id":988556,"name":"Flood Vulnerability Mapping","url":"https://www.academia.edu/Documents/in/Flood_Vulnerability_Mapping?f_ri=22613"},{"id":988563,"name":"Environmental Impact Asssessment","url":"https://www.academia.edu/Documents/in/Environmental_Impact_Asssessment?f_ri=22613"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_41572491" data-work_id="41572491" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/41572491/The_Distributions_of_Beta_Generated_and_Kumaraswamy_Generalized_Families_A_Brief_Survey">The Distributions of Beta-Generated and Kumaraswamy-Generalized Families: A Brief Survey</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In the last few years, several generators have been proposed to generate new distributions. Eugene et al. (2002) introduced the beta-generated family of distributions. By following the same logic, (Jones 2009) and Cordeiro and de Castro... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_41572491" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In the last few years, several generators have been proposed to generate new distributions. Eugene et al. (2002) introduced the beta-generated family of distributions. By following the same logic, (Jones 2009) and Cordeiro and de Castro (2011) introduced the Kumaraswamy-generalized family of distributions. These two families have been extensively used to generate new distributions. The main purpose of this study is to collect and record all existing distributions that clearly belong to the families of beta-generated ‎and Kumaraswamy-generalized distributions.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/41572491" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f451cfb9121aa4e2fd19dfb6b1d600c9" rel="nofollow" data-download="{"attachment_id":61728935,"asset_id":41572491,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/61728935/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="6996034" href="https://al-azhar.academia.edu/MahmoudAliSelim">Mahmoud Selim Alsanea</a><script data-card-contents-for-user="6996034" type="text/json">{"id":6996034,"first_name":"Mahmoud","last_name":"Selim Alsanea","domain_name":"al-azhar","page_name":"MahmoudAliSelim","display_name":"Mahmoud Selim Alsanea","profile_url":"https://al-azhar.academia.edu/MahmoudAliSelim?f_ri=22613","photo":"https://0.academia-photos.com/6996034/2658781/3095256/s65_mahmoud.selim.jpg"}</script></span></span></li><li class="js-paper-rank-work_41572491 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="41572491"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 41572491, container: ".js-paper-rank-work_41572491", }); 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Eugene et al. (2002) introduced the beta-generated family of distributions. By following the same logic, (Jones 2009) and Cordeiro and de Castro (2011) introduced the Kumaraswamy-generalized family of distributions. These two families have been extensively used to generate new distributions. The main purpose of this study is to collect and record all existing distributions that clearly belong to the families of beta-generated ‎and Kumaraswamy-generalized distributions.","downloadable_attachments":[{"id":61728935,"asset_id":41572491,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":6996034,"first_name":"Mahmoud","last_name":"Selim Alsanea","domain_name":"al-azhar","page_name":"MahmoudAliSelim","display_name":"Mahmoud Selim Alsanea","profile_url":"https://al-azhar.academia.edu/MahmoudAliSelim?f_ri=22613","photo":"https://0.academia-photos.com/6996034/2658781/3095256/s65_mahmoud.selim.jpg"}],"research_interests":[{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=22613","nofollow":false},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false},{"id":44293,"name":"Literature Review","url":"https://www.academia.edu/Documents/in/Literature_Review?f_ri=22613","nofollow":false},{"id":56056,"name":"Distribution Theory","url":"https://www.academia.edu/Documents/in/Distribution_Theory?f_ri=22613","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_71202050" data-work_id="71202050" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/71202050/Chi_Square_Mixture_of_Transformed_Inverse_Transformed_Gamma_Family">Chi-Square Mixture of Transformed/Inverse Transformed Gamma Family</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/71202050" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="212731747" href="https://independent.academia.edu/MiralunaLHerrera">Miraluna L. 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L'uso di questi test permette di valutare alcuni tipi di abilità come la rapidità... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_34619659" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Sunto Nei corsi di Probabilità e Statistica, un metodo molto diffuso per la valutazione degli studenti consiste nel sottoporli a quiz a risposta multipla. L'uso di questi test permette di valutare alcuni tipi di abilità come la rapidità di risposta, la memoria a breve termine, la lucidità mentale e l'attitudine a gareggiare. A nostro parere, la verifica attraverso i test può essere sicuramente utile per l'analisi di alcuni aspetti e per velocizzare il percorso di valutazione ma si deve essere consapevoli dei limiti di una tale procedura standardizzata e quindi escludere che le valutazioni di alunni, classi e scuole possano essere ridotte a elaborazioni di risultati di test. A dimostrazione di questa tesi, questo articolo argomenta in dettaglio i limiti principali dei test, presenta alcuni recenti modelli proposti in letteratura e propone alcuni metodi di valutazione alternativi. Paole Chiave: item responce theory, valutazione, test, probabilità 1. Introduzione Nei test per la valutazione dell'apprendimento della Probabilità e della Statistica, un possibile metodo di valutazione consiste nel sottoporre gli studenti a quiz a risposta multipla. Se ad esempio le possibili risposte sono quattro, si può pensare che, una risposta sia quella esatta, una sia quella del tutto sbagliata, le altre due possano sembrare esatte a un individuo poco attento, o poco preparato, o poco abile, e determinano, dunque, la selettività del quesito. L'uso dei test permette di valutare alcuni tipi di abilità come ad esempio:</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/34619659" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8c322b4d7daf7eb6a63313dc4e32c03a" rel="nofollow" data-download="{"attachment_id":54484452,"asset_id":34619659,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/54484452/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="6946936" href="https://unimercatorum.academia.edu/FabrizioMaturo">Fabrizio Maturo</a><script data-card-contents-for-user="6946936" type="text/json">{"id":6946936,"first_name":"Fabrizio","last_name":"Maturo","domain_name":"unimercatorum","page_name":"FabrizioMaturo","display_name":"Fabrizio Maturo","profile_url":"https://unimercatorum.academia.edu/FabrizioMaturo?f_ri=22613","photo":"https://0.academia-photos.com/6946936/8851567/99519629/s65_fabrizio.maturo.jpg"}</script></span></span></li><li class="js-paper-rank-work_34619659 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="34619659"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 34619659, container: ".js-paper-rank-work_34619659", }); });</script></li><li class="js-percentile-work_34619659 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 34619659; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_34619659"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_34619659 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="34619659"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 34619659; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=34619659]").text(description); $(".js-view-count-work_34619659").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_34619659").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="34619659"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i></div><span class="InlineList-item-text u-textTruncate u-pl6x"><a class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a><script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (false) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=34619659]'), work: {"id":34619659,"title":"Quesiti e test di Probabilità e Statistica: un'analisi critica","created_at":"2017-09-20T07:03:04.286-07:00","url":"https://www.academia.edu/34619659/Quesiti_e_test_di_Probabilit%C3%A0_e_Statistica_unanalisi_critica?f_ri=22613","dom_id":"work_34619659","summary":"Sunto Nei corsi di Probabilità e Statistica, un metodo molto diffuso per la valutazione degli studenti consiste nel sottoporli a quiz a risposta multipla. L'uso di questi test permette di valutare alcuni tipi di abilità come la rapidità di risposta, la memoria a breve termine, la lucidità mentale e l'attitudine a gareggiare. A nostro parere, la verifica attraverso i test può essere sicuramente utile per l'analisi di alcuni aspetti e per velocizzare il percorso di valutazione ma si deve essere consapevoli dei limiti di una tale procedura standardizzata e quindi escludere che le valutazioni di alunni, classi e scuole possano essere ridotte a elaborazioni di risultati di test. A dimostrazione di questa tesi, questo articolo argomenta in dettaglio i limiti principali dei test, presenta alcuni recenti modelli proposti in letteratura e propone alcuni metodi di valutazione alternativi. Paole Chiave: item responce theory, valutazione, test, probabilità 1. Introduzione Nei test per la valutazione dell'apprendimento della Probabilità e della Statistica, un possibile metodo di valutazione consiste nel sottoporre gli studenti a quiz a risposta multipla. Se ad esempio le possibili risposte sono quattro, si può pensare che, una risposta sia quella esatta, una sia quella del tutto sbagliata, le altre due possano sembrare esatte a un individuo poco attento, o poco preparato, o poco abile, e determinano, dunque, la selettività del quesito. L'uso dei test permette di valutare alcuni tipi di abilità come ad esempio:","downloadable_attachments":[{"id":54484452,"asset_id":34619659,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":6946936,"first_name":"Fabrizio","last_name":"Maturo","domain_name":"unimercatorum","page_name":"FabrizioMaturo","display_name":"Fabrizio Maturo","profile_url":"https://unimercatorum.academia.edu/FabrizioMaturo?f_ri=22613","photo":"https://0.academia-photos.com/6946936/8851567/99519629/s65_fabrizio.maturo.jpg"}],"research_interests":[{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_37264412" data-work_id="37264412" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/37264412/Notes_on_Linear_Statistical_Models">Notes on Linear Statistical Models</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">• Models for dependence of one variable on some other variable or variables • Examples-smoking and life expectancy-diet and weight or BMI-height of parents and height of the child- ...</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/37264412" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ad76e15e77394a4fd334a2044dddc941" rel="nofollow" data-download="{"attachment_id":57218157,"asset_id":37264412,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/57218157/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="2792810" href="https://independentresearcher.academia.edu/DrJMAshfaqueAMIMAMInstP">Dr. J. M. Ashfaque (MInstP)</a><script data-card-contents-for-user="2792810" type="text/json">{"id":2792810,"first_name":"Dr. J. M.","last_name":"Ashfaque (MInstP)","domain_name":"independentresearcher","page_name":"DrJMAshfaqueAMIMAMInstP","display_name":"Dr. J. M. Ashfaque (MInstP)","profile_url":"https://independentresearcher.academia.edu/DrJMAshfaqueAMIMAMInstP?f_ri=22613","photo":"https://0.academia-photos.com/2792810/914293/18370870/s65_dr._j._m..ashfaque_amima_minstp_.jpg"}</script></span></span></li><li class="js-paper-rank-work_37264412 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="37264412"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 37264412, container: ".js-paper-rank-work_37264412", }); });</script></li><li class="js-percentile-work_37264412 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 37264412; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_37264412"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_37264412 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="37264412"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 37264412; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=37264412]").text(description); $(".js-view-count-work_37264412").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_37264412").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="37264412"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="305" href="https://www.academia.edu/Documents/in/Applied_Mathematics">Applied Mathematics</a>, <script data-card-contents-for-ri="305" type="text/json">{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="892" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4060" href="https://www.academia.edu/Documents/in/Applied_Statistics">Applied Statistics</a>, <script data-card-contents-for-ri="4060" type="text/json">{"id":4060,"name":"Applied Statistics","url":"https://www.academia.edu/Documents/in/Applied_Statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="19997" href="https://www.academia.edu/Documents/in/Pure_Mathematics">Pure Mathematics</a><script data-card-contents-for-ri="19997" type="text/json">{"id":19997,"name":"Pure Mathematics","url":"https://www.academia.edu/Documents/in/Pure_Mathematics?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=37264412]'), work: {"id":37264412,"title":"Notes on Linear Statistical Models","created_at":"2018-08-20T07:02:45.888-07:00","url":"https://www.academia.edu/37264412/Notes_on_Linear_Statistical_Models?f_ri=22613","dom_id":"work_37264412","summary":"• Models for dependence of one variable on some other variable or variables • Examples-smoking and life expectancy-diet and weight or BMI-height of parents and height of the child- ...","downloadable_attachments":[{"id":57218157,"asset_id":37264412,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":2792810,"first_name":"Dr. J. M.","last_name":"Ashfaque (MInstP)","domain_name":"independentresearcher","page_name":"DrJMAshfaqueAMIMAMInstP","display_name":"Dr. J. M. Ashfaque (MInstP)","profile_url":"https://independentresearcher.academia.edu/DrJMAshfaqueAMIMAMInstP?f_ri=22613","photo":"https://0.academia-photos.com/2792810/914293/18370870/s65_dr._j._m..ashfaque_amima_minstp_.jpg"}],"research_interests":[{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=22613","nofollow":false},{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=22613","nofollow":false},{"id":4060,"name":"Applied Statistics","url":"https://www.academia.edu/Documents/in/Applied_Statistics?f_ri=22613","nofollow":false},{"id":19997,"name":"Pure Mathematics","url":"https://www.academia.edu/Documents/in/Pure_Mathematics?f_ri=22613","nofollow":false},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613"},{"id":802170,"name":"Mathemaics","url":"https://www.academia.edu/Documents/in/Mathemaics?f_ri=22613"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_64536926" data-work_id="64536926" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/64536926/Properties_Inference_and_Applications_of_Alpha_Power_Extended_Inverted_Weibull_Distribution">Properties, Inference and Applications of Alpha Power Extended Inverted Weibull Distribution</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this work, we introduce a new generalization of the Inverted Weibull distribution called the alpha power Extended Inverted Weibull distribution using the alpha power transformation method. This approach adds an extra parameter to the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_64536926" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this work, we introduce a new generalization of the Inverted Weibull distribution called the alpha power Extended Inverted Weibull distribution using the alpha power transformation method. This approach adds an extra parameter to the baseline distribution. The statistical properties of this distribution including the mean, variance, coefficient of variation, quantile function, median, ordinary and incomplete moments, skewness, kurtosis, moment and moment generating functions, reliability analysis, Lorenz and Bonferroni and curves, Ré nyi of entropy and order statistics are studied. We consider the method of maximum likelihood for estimating the model parameters and the observed information matrix is derived. Simulation method and three real life data sets are presented to demonstrate the effectiveness of the new model.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/64536926" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="612dc841681363dbd46c6101d084d1b3" rel="nofollow" data-download="{"attachment_id":76528863,"asset_id":64536926,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/76528863/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="66038690" href="https://pgschool.academia.edu/adebisiade">Adebisi A . Ogunde</a><script data-card-contents-for-user="66038690" type="text/json">{"id":66038690,"first_name":"Adebisi","last_name":"Ogunde","domain_name":"pgschool","page_name":"adebisiade","display_name":"Adebisi A . 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This approach adds an extra parameter to the baseline distribution. The statistical properties of this distribution including the mean, variance, coefficient of variation, quantile function, median, ordinary and incomplete moments, skewness, kurtosis, moment and moment generating functions, reliability analysis, Lorenz and Bonferroni and curves, Ré nyi of entropy and order statistics are studied. We consider the method of maximum likelihood for estimating the model parameters and the observed information matrix is derived. Simulation method and three real life data sets are presented to demonstrate the effectiveness of the new model.","downloadable_attachments":[{"id":76528863,"asset_id":64536926,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":66038690,"first_name":"Adebisi","last_name":"Ogunde","domain_name":"pgschool","page_name":"adebisiade","display_name":"Adebisi A . Ogunde","profile_url":"https://pgschool.academia.edu/adebisiade?f_ri=22613","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=22613","nofollow":false},{"id":16682,"name":"Mathematical Modelling","url":"https://www.academia.edu/Documents/in/Mathematical_Modelling?f_ri=22613","nofollow":false},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false},{"id":32815,"name":"Probability and Mathematical Statistics in power systems","url":"https://www.academia.edu/Documents/in/Probability_and_Mathematical_Statistics_in_power_systems?f_ri=22613","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_80452681" data-work_id="80452681" itemscope="itemscope" 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type="text/json">{"id":369,"name":"Calculus","url":"https://www.academia.edu/Documents/in/Calculus?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a>, <script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="33069" href="https://www.academia.edu/Documents/in/Probability">Probability</a><script data-card-contents-for-ri="33069" type="text/json">{"id":33069,"name":"Probability","url":"https://www.academia.edu/Documents/in/Probability?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=80452681]'), work: {"id":80452681,"title":"Approximate Function Inversion by Series Expansions","created_at":"2022-06-01T06:11:42.320-07:00","url":"https://www.academia.edu/80452681/Approximate_Function_Inversion_by_Series_Expansions?f_ri=22613","dom_id":"work_80452681","summary":null,"downloadable_attachments":[{"id":86829460,"asset_id":80452681,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":57882510,"first_name":"Hugo","last_name":"Hernandez","domain_name":"independent","page_name":"HugoHernandez97","display_name":"Hugo Hernandez","profile_url":"https://independent.academia.edu/HugoHernandez97?f_ri=22613","photo":"https://0.academia-photos.com/57882510/15170791/15861515/s65_hugo.hernandez.png"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=22613","nofollow":false},{"id":369,"name":"Calculus","url":"https://www.academia.edu/Documents/in/Calculus?f_ri=22613","nofollow":false},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false},{"id":33069,"name":"Probability","url":"https://www.academia.edu/Documents/in/Probability?f_ri=22613","nofollow":false},{"id":156644,"name":"Series expansions","url":"https://www.academia.edu/Documents/in/Series_expansions?f_ri=22613"},{"id":394451,"name":"Taylor Series","url":"https://www.academia.edu/Documents/in/Taylor_Series?f_ri=22613"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_78635329" data-work_id="78635329" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/78635329/Statistics_III_Probability_and_statistical_tests">Statistics III: Probability and statistical tests</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/78635329" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a 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class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78635329; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_78635329"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_78635329 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="78635329"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78635329; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78635329]").text(description); $(".js-view-count-work_78635329").attr('title', 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data-card-contents-for-ri="26327" type="text/json">{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=78635329]'), work: {"id":78635329,"title":"Statistics III: Probability and statistical tests","created_at":"2022-05-06T16:33:07.484-07:00","url":"https://www.academia.edu/78635329/Statistics_III_Probability_and_statistical_tests?f_ri=22613","dom_id":"work_78635329","summary":null,"downloadable_attachments":[{"id":85615112,"asset_id":78635329,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":11071445,"first_name":"Abdul Ghaaliq","last_name":"Lalkhen","domain_name":"independent","page_name":"AbdulGhaaliqLalkhen","display_name":"Abdul Ghaaliq Lalkhen","profile_url":"https://independent.academia.edu/AbdulGhaaliqLalkhen?f_ri=22613","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=22613","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_75889084" data-work_id="75889084" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/75889084/An_Application_on_Ensemble_Learning_Using_KNIME">An Application on Ensemble Learning Using KNIME</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Machine learning is the science of computers behaving and learning like humans with the knowledge and data of people’s observations, without being directly programmed. In fact, machine learning is inspired by the learning processes of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_75889084" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Machine learning is the science of computers behaving and learning like humans with the knowledge and data of people’s observations, without being directly programmed. In fact, machine learning is inspired by the learning processes of humans. Among machine learning methods, Bayes’ theorem is an important subject studied in probability theory.Naive Bayes classifier is based on Bayes theorem. The way the algorithm works is that it calculates the probability of each state for an element and classifies it according to its highest probability value. Decision tree is a method based on classification by decomposing the data set according to common features. It consists of “branches”, “leaves” and “roots”, just like real-world trees. In decision trees, the superstructure is the root and the substructure is the leaves. It creates a structure that allows the branches to decide between the root and the leaf. Ensemble learning algorithms improve classification performance by combining many machine learning methods. In this study, decision trees from data mining techniques and naive bayes technique were applied on 215 data set “Academic and Employability Factors Affecting Placement”. As a result of this study, the decision tree accuracy rate is 91,892, the naive bayes accuracy rate is 94,595 and the ensemble learning result is 97,297. Thus, a better result is obtained than the result of both algorithms used. The program is implemented on “Knime” program called as “end-to-end data science”.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/75889084" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ed58fe3ce7e0019514d969e44b033b0c" rel="nofollow" data-download="{"attachment_id":83777533,"asset_id":75889084,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/83777533/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="174391068" href="https://independent.academia.edu/hilalcelik5">hilal celik</a><script data-card-contents-for-user="174391068" type="text/json">{"id":174391068,"first_name":"hilal","last_name":"celik","domain_name":"independent","page_name":"hilalcelik5","display_name":"hilal celik","profile_url":"https://independent.academia.edu/hilalcelik5?f_ri=22613","photo":"https://0.academia-photos.com/174391068/86527673/75193950/s65_hilal.celik.jpg"}</script></span></span></li><li class="js-paper-rank-work_75889084 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="75889084"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 75889084, container: ".js-paper-rank-work_75889084", }); 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$(".js-view-count[data-work-id=75889084]").text(description); $(".js-view-count-work_75889084").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_75889084").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="75889084"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2009" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a>, <script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="69100" href="https://www.academia.edu/Documents/in/Data_Science">Data Science</a>, <script data-card-contents-for-ri="69100" type="text/json">{"id":69100,"name":"Data Science","url":"https://www.academia.edu/Documents/in/Data_Science?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="1758619" href="https://www.academia.edu/Documents/in/KNIME">KNIME</a><script data-card-contents-for-ri="1758619" type="text/json">{"id":1758619,"name":"KNIME","url":"https://www.academia.edu/Documents/in/KNIME?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=75889084]'), work: {"id":75889084,"title":"An Application on Ensemble Learning Using KNIME","created_at":"2022-04-09T01:54:20.182-07:00","url":"https://www.academia.edu/75889084/An_Application_on_Ensemble_Learning_Using_KNIME?f_ri=22613","dom_id":"work_75889084","summary":"Machine learning is the science of computers behaving and learning like humans with the knowledge and data of people’s observations, without being directly programmed. In fact, machine learning is inspired by the learning processes of humans. Among machine learning methods, Bayes’ theorem is an important subject studied in probability theory.Naive Bayes classifier is based on Bayes theorem. The way the algorithm works is that it calculates the probability of each state for an element and classifies it according to its highest probability value. Decision tree is a method based on classification by decomposing the data set according to common features. It consists of “branches”, “leaves” and “roots”, just like real-world trees. In decision trees, the superstructure is the root and the substructure is the leaves. It creates a structure that allows the branches to decide between the root and the leaf. Ensemble learning algorithms improve classification performance by combining many machine learning methods. In this study, decision trees from data mining techniques and naive bayes technique were applied on 215 data set “Academic and Employability Factors Affecting Placement”. As a result of this study, the decision tree accuracy rate is 91,892, the naive bayes accuracy rate is 94,595 and the ensemble learning result is 97,297. Thus, a better result is obtained than the result of both algorithms used. The program is implemented on “Knime” program called as “end-to-end data science”.","downloadable_attachments":[{"id":83777533,"asset_id":75889084,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":174391068,"first_name":"hilal","last_name":"celik","domain_name":"independent","page_name":"hilalcelik5","display_name":"hilal celik","profile_url":"https://independent.academia.edu/hilalcelik5?f_ri=22613","photo":"https://0.academia-photos.com/174391068/86527673/75193950/s65_hilal.celik.jpg"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=22613","nofollow":false},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false},{"id":69100,"name":"Data Science","url":"https://www.academia.edu/Documents/in/Data_Science?f_ri=22613","nofollow":false},{"id":1758619,"name":"KNIME","url":"https://www.academia.edu/Documents/in/KNIME?f_ri=22613","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_43441645" data-work_id="43441645" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/43441645/Standardising_Dementia_Diagnosis_Across_Linguistic_and_Educational_Diversity_Study_Design_of_the_Indian_Council_of_Medical_Research_Neurocognitive_Tool_Box_ICMR_NCTB">Standardising Dementia Diagnosis Across Linguistic and Educational Diversity: Study Design of the Indian Council of Medical Research-Neurocognitive Tool Box (ICMR-NCTB</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Objectives: While the burden of dementia is increasing in low-and middle-income countries, there is a low rate of diagnosis and paucity of research in these regions. A major challenge to study dementia is the limited availability of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_43441645" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Objectives: While the burden of dementia is increasing in low-and middle-income countries, there is a low rate of diagnosis and paucity of research in these regions. A major challenge to study dementia is the limited availability of standardised diagnostic tools for use in populations with linguistic and educational diversity. The objectives of the study were to develop a standardised and comprehensive neurocognitive test battery to diagnose dementia and mild cognitive impairment (MCI) due to varied etiologies, across different languages and educational levels in India, to facilitate research efforts in diverse settings. Methods: A multidisciplinary expert group formed by Indian Council of Medical Research (ICMR) collaborated towards adapting and validating a neurocognitive test battery, that is, the ICMR Neurocognitive Tool Box (ICMR-NCTB) in five Indian languages (Hindi, Bengali, Telugu, Kannada, and Malayalam), for illiterates and literates, to standardise diagnosis of dementia and MCI in India. Results: Following a review of existing international and national efforts at standardising dementia diagnosis, the ICMR-NCTB was developed and adapted to the Indian setting of sociolinguistic diversity. The battery consisted of tests of cognition, behaviour, and functional activities. A uniform protocol for diagnosis of normal cognition, MCI, and dementia due to neurodegenerative diseases and stroke was followed in six centres. A systematic plan for validating the ICMR-NCTB and establishing cutoff values in a diverse multicentric cohort was developed. Conclusions: A key outcome was the development of a comprehensive diagnostic tool for diagnosis of dementia and MCI due to varied etiologies, in the diverse socio-demographic setting of India.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/43441645" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="aa88e2b62c3e8b738a1fad250179a9f9" rel="nofollow" data-download="{"attachment_id":63746938,"asset_id":43441645,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/63746938/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="162187755" href="https://positvepsychology.academia.edu/FebaVarghese">Feba Varghese</a><script data-card-contents-for-user="162187755" type="text/json">{"id":162187755,"first_name":"Feba","last_name":"Varghese","domain_name":"positvepsychology","page_name":"FebaVarghese","display_name":"Feba Varghese","profile_url":"https://positvepsychology.academia.edu/FebaVarghese?f_ri=22613","photo":"https://0.academia-photos.com/162187755/45510615/35466191/s65_feba.varghese.jpg"}</script></span></span></li><li class="js-paper-rank-work_43441645 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="43441645"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 43441645, container: ".js-paper-rank-work_43441645", }); 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$(".js-view-count[data-work-id=43441645]").text(description); $(".js-view-count-work_43441645").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_43441645").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="43441645"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="892" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="1085" href="https://www.academia.edu/Documents/in/Epidemiology">Epidemiology</a>, <script data-card-contents-for-ri="1085" type="text/json">{"id":1085,"name":"Epidemiology","url":"https://www.academia.edu/Documents/in/Epidemiology?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="10610" href="https://www.academia.edu/Documents/in/Survival_Analysis">Survival Analysis</a>, <script data-card-contents-for-ri="10610" type="text/json">{"id":10610,"name":"Survival Analysis","url":"https://www.academia.edu/Documents/in/Survival_Analysis?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a><script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=43441645]'), work: {"id":43441645,"title":"Standardising Dementia Diagnosis Across Linguistic and Educational Diversity: Study Design of the Indian Council of Medical Research-Neurocognitive Tool Box (ICMR-NCTB","created_at":"2020-06-26T05:12:47.770-07:00","url":"https://www.academia.edu/43441645/Standardising_Dementia_Diagnosis_Across_Linguistic_and_Educational_Diversity_Study_Design_of_the_Indian_Council_of_Medical_Research_Neurocognitive_Tool_Box_ICMR_NCTB?f_ri=22613","dom_id":"work_43441645","summary":"Objectives: While the burden of dementia is increasing in low-and middle-income countries, there is a low rate of diagnosis and paucity of research in these regions. A major challenge to study dementia is the limited availability of standardised diagnostic tools for use in populations with linguistic and educational diversity. The objectives of the study were to develop a standardised and comprehensive neurocognitive test battery to diagnose dementia and mild cognitive impairment (MCI) due to varied etiologies, across different languages and educational levels in India, to facilitate research efforts in diverse settings. Methods: A multidisciplinary expert group formed by Indian Council of Medical Research (ICMR) collaborated towards adapting and validating a neurocognitive test battery, that is, the ICMR Neurocognitive Tool Box (ICMR-NCTB) in five Indian languages (Hindi, Bengali, Telugu, Kannada, and Malayalam), for illiterates and literates, to standardise diagnosis of dementia and MCI in India. Results: Following a review of existing international and national efforts at standardising dementia diagnosis, the ICMR-NCTB was developed and adapted to the Indian setting of sociolinguistic diversity. The battery consisted of tests of cognition, behaviour, and functional activities. A uniform protocol for diagnosis of normal cognition, MCI, and dementia due to neurodegenerative diseases and stroke was followed in six centres. A systematic plan for validating the ICMR-NCTB and establishing cutoff values in a diverse multicentric cohort was developed. Conclusions: A key outcome was the development of a comprehensive diagnostic tool for diagnosis of dementia and MCI due to varied etiologies, in the diverse socio-demographic setting of India.","downloadable_attachments":[{"id":63746938,"asset_id":43441645,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":162187755,"first_name":"Feba","last_name":"Varghese","domain_name":"positvepsychology","page_name":"FebaVarghese","display_name":"Feba Varghese","profile_url":"https://positvepsychology.academia.edu/FebaVarghese?f_ri=22613","photo":"https://0.academia-photos.com/162187755/45510615/35466191/s65_feba.varghese.jpg"}],"research_interests":[{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=22613","nofollow":false},{"id":1085,"name":"Epidemiology","url":"https://www.academia.edu/Documents/in/Epidemiology?f_ri=22613","nofollow":false},{"id":10610,"name":"Survival Analysis","url":"https://www.academia.edu/Documents/in/Survival_Analysis?f_ri=22613","nofollow":false},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_42739856" data-work_id="42739856" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/42739856/Compilado_Covid_19_Revista_Quest%C3%A3o_de_Ci%C3%AAncia_">Compilado Covid-19 (Revista Questão de Ciência)</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Compilado Covid-19 (Revista Questão de Ciência) <a href="https://www.revistaquestaodeciencia.com.br/artigo/2020/03/22/crescimento-exponencial-da-covid-19-nao-e-fantasia" rel="nofollow">https://www.revistaquestaodeciencia.com.br/artigo/2020/03/22/crescimento-exponencial-da-covid-19-nao-e-fantasia</a>... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_42739856" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Compilado Covid-19 (Revista Questão de Ciência)<br /><br /><a href="https://www.revistaquestaodeciencia.com.br/artigo/2020/03/22/crescimento-exponencial-da-covid-19-nao-e-fantasia" rel="nofollow">https://www.revistaquestaodeciencia.com.br/artigo/2020/03/22/crescimento-exponencial-da-covid-19-nao-e-fantasia</a><br /><br /><a href="https://www.revistaquestaodeciencia.com.br/artigo/2020/03/24/tres-cenarios-para-o-coronavirus-no-brasil" 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To succeed in poker, it is not enough to simply anticipate the actions of other players and try to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_34567225" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Gambling and War: Risk, Reward, and Chance in International Conflict brings readers a war college course taught at a Las Vegas casino. To succeed in poker, it is not enough to simply anticipate the actions of other players and try to outsmart them. A successful player must also understand and appreciate the role of randomness. Additionally, players must confront the reality that all human beings are prone to errors in judgment, which cause them to make suboptimal choices under many circumstances. Taken together, these challenges make poker a fascinating and highly unpredictable game, much like the dynamics of international conflict. Any comprehensive analysis of why wars occur and how they are fought must consider a variety of factors including strategy, human error, and dumb luck.<br /><br />Gambling and War applies lessons learned from poker, blackjack, roulette, and other games of chance to the study of international conflict. Drawing on scholarly insights from a variety of fields, including probability, statistics, political science, psychology, and economics, the book offers thoughts on how we can better manage and prevent international conflict, the costliest game of all.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/34567225" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d641df06d69fd71dd485722eb32d4c79" rel="nofollow" data-download="{"attachment_id":54432757,"asset_id":34567225,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/54432757/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="47835185" href="https://uncc.academia.edu/JustinConrad">Justin Conrad</a><script data-card-contents-for-user="47835185" type="text/json">{"id":47835185,"first_name":"Justin","last_name":"Conrad","domain_name":"uncc","page_name":"JustinConrad","display_name":"Justin Conrad","profile_url":"https://uncc.academia.edu/JustinConrad?f_ri=22613","photo":"https://0.academia-photos.com/47835185/17727704/17761415/s65_justin.conrad.jpg"}</script></span></span></li><li class="js-paper-rank-work_34567225 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="34567225"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 34567225, container: ".js-paper-rank-work_34567225", }); 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To succeed in poker, it is not enough to simply anticipate the actions of other players and try to outsmart them. A successful player must also understand and appreciate the role of randomness. Additionally, players must confront the reality that all human beings are prone to errors in judgment, which cause them to make suboptimal choices under many circumstances. Taken together, these challenges make poker a fascinating and highly unpredictable game, much like the dynamics of international conflict. Any comprehensive analysis of why wars occur and how they are fought must consider a variety of factors including strategy, human error, and dumb luck.\n\nGambling and War applies lessons learned from poker, blackjack, roulette, and other games of chance to the study of international conflict. Drawing on scholarly insights from a variety of fields, including probability, statistics, political science, psychology, and economics, the book offers thoughts on how we can better manage and prevent international conflict, the costliest game of all.","downloadable_attachments":[{"id":54432757,"asset_id":34567225,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":47835185,"first_name":"Justin","last_name":"Conrad","domain_name":"uncc","page_name":"JustinConrad","display_name":"Justin Conrad","profile_url":"https://uncc.academia.edu/JustinConrad?f_ri=22613","photo":"https://0.academia-photos.com/47835185/17727704/17761415/s65_justin.conrad.jpg"}],"research_interests":[{"id":579,"name":"Strategy (Military Science)","url":"https://www.academia.edu/Documents/in/Strategy_Military_Science_?f_ri=22613","nofollow":false},{"id":797,"name":"International Relations","url":"https://www.academia.edu/Documents/in/International_Relations?f_ri=22613","nofollow":false},{"id":3872,"name":"War Studies","url":"https://www.academia.edu/Documents/in/War_Studies?f_ri=22613","nofollow":false},{"id":4486,"name":"Political Science","url":"https://www.academia.edu/Documents/in/Political_Science?f_ri=22613","nofollow":false},{"id":13805,"name":"Behavioral Economics","url":"https://www.academia.edu/Documents/in/Behavioral_Economics?f_ri=22613"},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613"},{"id":92164,"name":"Gambling","url":"https://www.academia.edu/Documents/in/Gambling?f_ri=22613"},{"id":140181,"name":"International conflicts","url":"https://www.academia.edu/Documents/in/International_conflicts?f_ri=22613"},{"id":278800,"name":"Bargaining Theory","url":"https://www.academia.edu/Documents/in/Bargaining_Theory?f_ri=22613"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_14044153" data-work_id="14044153" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/14044153/Aplicaci%C3%B3n_de_la_estad%C3%ADstica_aplicada_en_%C3%ADndice_de_desnutrici%C3%B3n">Aplicación de la estadística aplicada en índice de desnutrición</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">Aplicación de las probabilidades para hallar el índice de desnutrición en el distrito de San Juan de Lurigancho</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/14044153" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2f7cc78ee648480178f1aa041712f9f4" rel="nofollow" data-download="{"attachment_id":38188759,"asset_id":14044153,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/38188759/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="8878269" href="https://upn1.academia.edu/leoeduardobobadillaatao">Leo Eduardo Bobadilla Atao</a><script data-card-contents-for-user="8878269" type="text/json">{"id":8878269,"first_name":"Leo Eduardo","last_name":"Bobadilla Atao","domain_name":"upn1","page_name":"leoeduardobobadillaatao","display_name":"Leo Eduardo Bobadilla Atao","profile_url":"https://upn1.academia.edu/leoeduardobobadillaatao?f_ri=22613","photo":"https://0.academia-photos.com/8878269/2914797/95223668/s65_leo_eduardo.bobadilla_atao.jpeg"}</script></span></span></li><li class="js-paper-rank-work_14044153 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="14044153"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 14044153, container: ".js-paper-rank-work_14044153", }); 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Dempster in the context of statistical inference, to be later developed by Glenn Shafer as a general... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_37181239" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The theory of belief functions, sometimes referred to as evidence theory or Dempster-Shafer theory, was first introduced by Arthur P. Dempster in the context of statistical inference, to be later developed by Glenn Shafer as a general framework for modelling epistemic uncertainty. Belief theory and the closely related random set theory form natural frameworks for modelling situations in which data are missing or scarce: think of extremely rare events such as volcanic eruptions or power plant meltdowns, problems subject to huge uncertainties due to the number and complexity of the factors involved (e.g. climate change), but also the all-important issue with generalisation from small training sets in machine learning. This tutorial is designed to introduce the principles and rationale of random sets and belief function theory to mainstream statisticians, mathematicians and working scientists, survey the key elements of the methodology and the most recent developments, make practitioners aware of the set of tools that have been developed for reasoning in the belief function framework on real-world problems. Attendees will acquire first-hand knowledge of how to apply these tools to significant problems in major application fields such as computer vision, climate change, and others. A research programme for the future of random set theory and high impact applications is eventually outlined.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/37181239" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5724edce998fe0fa70b36555d1e45988" rel="nofollow" data-download="{"attachment_id":57132594,"asset_id":37181239,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/57132594/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="366407" href="https://oxfordbrookes.academia.edu/FabioCuzzolin">Fabio Cuzzolin</a><script data-card-contents-for-user="366407" type="text/json">{"id":366407,"first_name":"Fabio","last_name":"Cuzzolin","domain_name":"oxfordbrookes","page_name":"FabioCuzzolin","display_name":"Fabio Cuzzolin","profile_url":"https://oxfordbrookes.academia.edu/FabioCuzzolin?f_ri=22613","photo":"https://0.academia-photos.com/366407/112374/61740579/s65_fabio.cuzzolin.jpg"}</script></span></span></li><li class="js-paper-rank-work_37181239 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="37181239"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 37181239, container: ".js-paper-rank-work_37181239", }); 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Dempster in the context of statistical inference, to be later developed by Glenn Shafer as a general framework for modelling epistemic uncertainty. Belief theory and the closely related random set theory form natural frameworks for modelling situations in which data are missing or scarce: think of extremely rare events such as volcanic eruptions or power plant meltdowns, problems subject to huge uncertainties due to the number and complexity of the factors involved (e.g. climate change), but also the all-important issue with generalisation from small training sets in machine learning. This tutorial is designed to introduce the principles and rationale of random sets and belief function theory to mainstream statisticians, mathematicians and working scientists, survey the key elements of the methodology and the most recent developments, make practitioners aware of the set of tools that have been developed for reasoning in the belief function framework on real-world problems. Attendees will acquire first-hand knowledge of how to apply these tools to significant problems in major application fields such as computer vision, climate change, and others. A research programme for the future of random set theory and high impact applications is eventually outlined.","downloadable_attachments":[{"id":57132594,"asset_id":37181239,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":366407,"first_name":"Fabio","last_name":"Cuzzolin","domain_name":"oxfordbrookes","page_name":"FabioCuzzolin","display_name":"Fabio Cuzzolin","profile_url":"https://oxfordbrookes.academia.edu/FabioCuzzolin?f_ri=22613","photo":"https://0.academia-photos.com/366407/112374/61740579/s65_fabio.cuzzolin.jpg"}],"research_interests":[{"id":344,"name":"Probability Theory","url":"https://www.academia.edu/Documents/in/Probability_Theory?f_ri=22613","nofollow":false},{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=22613","nofollow":false},{"id":6404,"name":"Reasoning about Uncertainty","url":"https://www.academia.edu/Documents/in/Reasoning_about_Uncertainty?f_ri=22613","nofollow":false},{"id":16097,"name":"Decision Making Under Uncertainty","url":"https://www.academia.edu/Documents/in/Decision_Making_Under_Uncertainty?f_ri=22613","nofollow":false},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613"},{"id":31412,"name":"Probability and Mathematical Statistics","url":"https://www.academia.edu/Documents/in/Probability_and_Mathematical_Statistics?f_ri=22613"},{"id":33069,"name":"Probability","url":"https://www.academia.edu/Documents/in/Probability?f_ri=22613"},{"id":61603,"name":"Uncertainty","url":"https://www.academia.edu/Documents/in/Uncertainty?f_ri=22613"},{"id":94223,"name":"Imprecise Probability","url":"https://www.academia.edu/Documents/in/Imprecise_Probability?f_ri=22613"},{"id":94225,"name":"Belief Functions","url":"https://www.academia.edu/Documents/in/Belief_Functions?f_ri=22613"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_38929269" data-work_id="38929269" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/38929269/Gaming_Behaviors_survey_based_on_Factor_Analysis">Gaming Behaviors survey based on Factor Analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The report finds the prospects of the gaming community by accessing the gamer based upon age, ethnicity, occupation, annual household income and education qualifications. The data collected as part of this survey would require further... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_38929269" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The report finds the prospects of the gaming community by accessing the gamer based upon age, ethnicity, occupation, annual household income and education qualifications. The data collected as part of this survey would require further investigation and remedial action by management as certain variables provided does not contribute to the overall variance percentage in factorial analysis.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/38929269" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="33e7c9ad0c1a6eb769bc9c864e5da6ac" rel="nofollow" data-download="{"attachment_id":59030007,"asset_id":38929269,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/59030007/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="110329812" href="https://unt.academia.edu/AshwinElumalai">Ashwin Elumalai</a><script data-card-contents-for-user="110329812" type="text/json">{"id":110329812,"first_name":"Ashwin","last_name":"Elumalai","domain_name":"unt","page_name":"AshwinElumalai","display_name":"Ashwin Elumalai","profile_url":"https://unt.academia.edu/AshwinElumalai?f_ri=22613","photo":"https://0.academia-photos.com/110329812/25969158/24613655/s65_ashwin.elumalai.png"}</script></span></span></li><li class="js-paper-rank-work_38929269 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="38929269"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 38929269, container: ".js-paper-rank-work_38929269", }); });</script></li><li class="js-percentile-work_38929269 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 38929269; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_38929269"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_38929269 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="38929269"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 38929269; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=38929269]").text(description); $(".js-view-count-work_38929269").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_38929269").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="38929269"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5486" href="https://www.academia.edu/Documents/in/Clustering_and_Classification_Methods">Clustering and Classification Methods</a>, <script data-card-contents-for-ri="5486" type="text/json">{"id":5486,"name":"Clustering and Classification Methods","url":"https://www.academia.edu/Documents/in/Clustering_and_Classification_Methods?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a>, <script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="51168" href="https://www.academia.edu/Documents/in/Predictive_Analytics">Predictive Analytics</a>, <script data-card-contents-for-ri="51168" type="text/json">{"id":51168,"name":"Predictive Analytics","url":"https://www.academia.edu/Documents/in/Predictive_Analytics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="151407" href="https://www.academia.edu/Documents/in/Exploratory_Factor_Analysis">Exploratory Factor Analysis</a><script data-card-contents-for-ri="151407" type="text/json">{"id":151407,"name":"Exploratory Factor Analysis","url":"https://www.academia.edu/Documents/in/Exploratory_Factor_Analysis?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=38929269]'), work: {"id":38929269,"title":"Gaming Behaviors survey based on Factor Analysis","created_at":"2019-04-25T09:59:04.063-07:00","url":"https://www.academia.edu/38929269/Gaming_Behaviors_survey_based_on_Factor_Analysis?f_ri=22613","dom_id":"work_38929269","summary":"The report finds the prospects of the gaming community by accessing the gamer based upon age, ethnicity, occupation, annual household income and education qualifications. The data collected as part of this survey would require further investigation and remedial action by management as certain variables provided does not contribute to the overall variance percentage in factorial analysis. ","downloadable_attachments":[{"id":59030007,"asset_id":38929269,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":110329812,"first_name":"Ashwin","last_name":"Elumalai","domain_name":"unt","page_name":"AshwinElumalai","display_name":"Ashwin Elumalai","profile_url":"https://unt.academia.edu/AshwinElumalai?f_ri=22613","photo":"https://0.academia-photos.com/110329812/25969158/24613655/s65_ashwin.elumalai.png"}],"research_interests":[{"id":5486,"name":"Clustering and Classification Methods","url":"https://www.academia.edu/Documents/in/Clustering_and_Classification_Methods?f_ri=22613","nofollow":false},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false},{"id":51168,"name":"Predictive Analytics","url":"https://www.academia.edu/Documents/in/Predictive_Analytics?f_ri=22613","nofollow":false},{"id":151407,"name":"Exploratory Factor Analysis","url":"https://www.academia.edu/Documents/in/Exploratory_Factor_Analysis?f_ri=22613","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_10872572" data-work_id="10872572" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/10872572/Uncertainty_and_information">Uncertainty and information</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Uncertainty and information are ideas that have a central role in contemporary economics in the domain of decision theory. The expected utility hypothesis is a powerful instruments widely used in theoretical and empirical analysis.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_10872572" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Uncertainty and information are ideas that have a central role in contemporary economics in the domain of decision theory. The expected utility hypothesis is a powerful instruments widely used in theoretical and empirical analysis. Contemporary economics considers the theme of uncertainty as a branch of decision theory. This is not a necessary result of a linear history, rather of an intellectual path that was not easily foreseable at the end of Nineteenth Century. The leading role in this story is not played by utility, as is usual in the traditional reconstructions of historians of economic thought, but by probability. Uncertainty is in fact a multifaceted concept. It refers to a subjective condition or a mental status of an agent not knowing for certain the consequences of a present or a future event (subjective uncertainty). It refers also to an objective status of things that may results in different outcomes (frequency of occurences), or are knowable only through careful measurements subjected to errors (objective uncertainty). Because the modern developments could happen, two conditions were required: both objective and subjective uncertainty needed to be treated with the device of probability. Both these recognitions slowly emerged between 17th and 20th century.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/10872572" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="18aed6723e4f7920468bdbf757fba9a4" rel="nofollow" data-download="{"attachment_id":36670455,"asset_id":10872572,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/36670455/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="878548" href="https://unisi.academia.edu/AlbertoBaccini">Alberto Baccini</a><script data-card-contents-for-user="878548" type="text/json">{"id":878548,"first_name":"Alberto","last_name":"Baccini","domain_name":"unisi","page_name":"AlbertoBaccini","display_name":"Alberto Baccini","profile_url":"https://unisi.academia.edu/AlbertoBaccini?f_ri=22613","photo":"https://0.academia-photos.com/878548/318292/377286/s65_alberto.baccini.jpg"}</script></span></span></li><li class="js-paper-rank-work_10872572 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="10872572"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 10872572, container: ".js-paper-rank-work_10872572", }); });</script></li><li class="js-percentile-work_10872572 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 10872572; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_10872572"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_10872572 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="10872572"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 10872572; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=10872572]").text(description); $(".js-view-count-work_10872572").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_10872572").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="10872572"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="1735" href="https://www.academia.edu/Documents/in/History_of_Economic_Thought">History of Economic Thought</a>, <script data-card-contents-for-ri="1735" type="text/json">{"id":1735,"name":"History of Economic Thought","url":"https://www.academia.edu/Documents/in/History_of_Economic_Thought?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="9796" href="https://www.academia.edu/Documents/in/Philosophy_Of_Probability">Philosophy Of Probability</a>, <script data-card-contents-for-ri="9796" type="text/json">{"id":9796,"name":"Philosophy Of Probability","url":"https://www.academia.edu/Documents/in/Philosophy_Of_Probability?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="12163" href="https://www.academia.edu/Documents/in/Augustin_Cournot">Augustin Cournot</a>, <script data-card-contents-for-ri="12163" type="text/json">{"id":12163,"name":"Augustin Cournot","url":"https://www.academia.edu/Documents/in/Augustin_Cournot?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="14243" href="https://www.academia.edu/Documents/in/History_of_Economics">History of Economics</a><script data-card-contents-for-ri="14243" type="text/json">{"id":14243,"name":"History of Economics","url":"https://www.academia.edu/Documents/in/History_of_Economics?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=10872572]'), work: {"id":10872572,"title":"Uncertainty and information","created_at":"2015-02-17T09:37:57.872-08:00","url":"https://www.academia.edu/10872572/Uncertainty_and_information?f_ri=22613","dom_id":"work_10872572","summary":"Uncertainty and information are ideas that have a central role in contemporary economics in the domain of decision theory. The expected utility hypothesis is a powerful instruments widely used in theoretical and empirical analysis. Contemporary economics considers the theme of uncertainty as a branch of decision theory. This is not a necessary result of a linear history, rather of an intellectual path that was not easily foreseable at the end of Nineteenth Century. The leading role in this story is not played by utility, as is usual in the traditional reconstructions of historians of economic thought, but by probability. Uncertainty is in fact a multifaceted concept. It refers to a subjective condition or a mental status of an agent not knowing for certain the consequences of a present or a future event (subjective uncertainty). It refers also to an objective status of things that may results in different outcomes (frequency of occurences), or are knowable only through careful measurements subjected to errors (objective uncertainty). Because the modern developments could happen, two conditions were required: both objective and subjective uncertainty needed to be treated with the device of probability. Both these recognitions slowly emerged between 17th and 20th century. ","downloadable_attachments":[{"id":36670455,"asset_id":10872572,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":878548,"first_name":"Alberto","last_name":"Baccini","domain_name":"unisi","page_name":"AlbertoBaccini","display_name":"Alberto Baccini","profile_url":"https://unisi.academia.edu/AlbertoBaccini?f_ri=22613","photo":"https://0.academia-photos.com/878548/318292/377286/s65_alberto.baccini.jpg"}],"research_interests":[{"id":1735,"name":"History of Economic Thought","url":"https://www.academia.edu/Documents/in/History_of_Economic_Thought?f_ri=22613","nofollow":false},{"id":9796,"name":"Philosophy Of Probability","url":"https://www.academia.edu/Documents/in/Philosophy_Of_Probability?f_ri=22613","nofollow":false},{"id":12163,"name":"Augustin Cournot","url":"https://www.academia.edu/Documents/in/Augustin_Cournot?f_ri=22613","nofollow":false},{"id":14243,"name":"History of Economics","url":"https://www.academia.edu/Documents/in/History_of_Economics?f_ri=22613","nofollow":false},{"id":16097,"name":"Decision Making Under Uncertainty","url":"https://www.academia.edu/Documents/in/Decision_Making_Under_Uncertainty?f_ri=22613"},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613"},{"id":31002,"name":"Blaise Pascal","url":"https://www.academia.edu/Documents/in/Blaise_Pascal?f_ri=22613"},{"id":157397,"name":"alfred Marshall","url":"https://www.academia.edu/Documents/in/alfred_Marshall?f_ri=22613"},{"id":287193,"name":"Daniel Bernoulli","url":"https://www.academia.edu/Documents/in/Daniel_Bernoulli?f_ri=22613"},{"id":384174,"name":"John Maynard Keynes","url":"https://www.academia.edu/Documents/in/John_Maynard_Keynes?f_ri=22613"},{"id":582439,"name":"History of Statistics and Probability","url":"https://www.academia.edu/Documents/in/History_of_Statistics_and_Probability?f_ri=22613"},{"id":1007821,"name":"Laplace","url":"https://www.academia.edu/Documents/in/Laplace?f_ri=22613"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_79535070" data-work_id="79535070" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/79535070/Interactions_Between_Water_Level_Crude_Oil_and_Hydroelectric_Power_Generation_in_Ghana_A_Structured_Vector_Auto_Regression_Approach">Interactions Between Water Level, Crude Oil, and Hydroelectric Power Generation in Ghana; A Structured Vector Auto Regression Approach</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Countries that suffer disturbances in their power generation are less likely to meet many of the sustainable development goals and general economic growth. This study used a three-variable SVAR model to examine the interactions of water... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_79535070" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Countries that suffer disturbances in their power generation are less likely to meet many of the sustainable development goals and general economic growth. This study used a three-variable SVAR model to examine the interactions of water level, crude oil and power generated from the Akosombo hydroelectric generation Dam in Ghana. Data used for this span from January 2010 to December 2019. From the results, none of the three important variables studied was found to be completely independent; dam level and crude oil are adjusted to absorb power generation shocks. All three variables drift away from their normal levels to contain shock before returning to their desired levels at varying time points. It has also been established that Dam water level shocks leads to a negative response in both power generation and crude oil in the short run. Overall, shocks to crude oil explains much of the variability in power generation than shocks to dam water level. These findings convey that there is...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/79535070" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="12a838e8fa965da71e78c5baa5da8e9c" rel="nofollow" data-download="{"attachment_id":86217290,"asset_id":79535070,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/86217290/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="210634064" href="https://independent.academia.edu/AkwasiAgyei2">Akwasi Agyei</a><script data-card-contents-for-user="210634064" type="text/json">{"id":210634064,"first_name":"Akwasi","last_name":"Agyei","domain_name":"independent","page_name":"AkwasiAgyei2","display_name":"Akwasi Agyei","profile_url":"https://independent.academia.edu/AkwasiAgyei2?f_ri=22613","photo":"https://0.academia-photos.com/210634064/70198329/58611734/s65_akwasi.agyei.png"}</script></span></span></li><li class="js-paper-rank-work_79535070 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="79535070"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 79535070, container: ".js-paper-rank-work_79535070", }); 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$(".js-view-count[data-work-id=79535070]").text(description); $(".js-view-count-work_79535070").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_79535070").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="79535070"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i></div><span class="InlineList-item-text u-textTruncate u-pl6x"><a class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a><script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (false) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=79535070]'), work: {"id":79535070,"title":"Interactions Between Water Level, Crude Oil, and Hydroelectric Power Generation in Ghana; A Structured Vector Auto Regression Approach","created_at":"2022-05-20T11:13:43.301-07:00","url":"https://www.academia.edu/79535070/Interactions_Between_Water_Level_Crude_Oil_and_Hydroelectric_Power_Generation_in_Ghana_A_Structured_Vector_Auto_Regression_Approach?f_ri=22613","dom_id":"work_79535070","summary":"Countries that suffer disturbances in their power generation are less likely to meet many of the sustainable development goals and general economic growth. This study used a three-variable SVAR model to examine the interactions of water level, crude oil and power generated from the Akosombo hydroelectric generation Dam in Ghana. Data used for this span from January 2010 to December 2019. From the results, none of the three important variables studied was found to be completely independent; dam level and crude oil are adjusted to absorb power generation shocks. All three variables drift away from their normal levels to contain shock before returning to their desired levels at varying time points. It has also been established that Dam water level shocks leads to a negative response in both power generation and crude oil in the short run. Overall, shocks to crude oil explains much of the variability in power generation than shocks to dam water level. These findings convey that there is...","downloadable_attachments":[{"id":86217290,"asset_id":79535070,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":210634064,"first_name":"Akwasi","last_name":"Agyei","domain_name":"independent","page_name":"AkwasiAgyei2","display_name":"Akwasi Agyei","profile_url":"https://independent.academia.edu/AkwasiAgyei2?f_ri=22613","photo":"https://0.academia-photos.com/210634064/70198329/58611734/s65_akwasi.agyei.png"}],"research_interests":[{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_9746576" data-work_id="9746576" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/9746576/Application_of_Probabilistic_Methods_to_Predict_Derby_Matches_in_the_English_Premier_League_EPL_">Application of Probabilistic Methods to Predict Derby Matches in the English Premier League (EPL)</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The Premier League is being broadcasted in 212 territories around the world, working with 80 different broadcasters. In addition, football is the world’s most popular game and constitutes the fastest growing gambling market. As a result,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_9746576" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The Premier League is being broadcasted in 212 territories around the world, working with 80 different broadcasters. In addition, football is the world’s most popular game and constitutes the fastest growing gambling market. As a result, researchers continue to introduce a variety of football models which are formulated by different forecast methodologies. In this report, we attempt to predict match outcomes of some famous derbies being played in EPL from December 2014 to May 2015. We present a Poisson distribution model for forecasting these matches using data from 2013-14 season till 24th November of 2014-15 season. Using this model we obtain 1) probability of who might win the game, 2) probability of different outcomes (number of goals scored by each team) and 3) if home advantage favours the home team. This model is an introduction to the predicting individual games can be developed further into a betting model to predict the season results and individual scores.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/9746576" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e8f0f1ac4f56840f028b8c7ccf7cffed" rel="nofollow" data-download="{"attachment_id":35929614,"asset_id":9746576,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/35929614/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="23436368" href="https://mun.academia.edu/CharlesNwaozuzu">Charles Nwaozuzu</a><script data-card-contents-for-user="23436368" type="text/json">{"id":23436368,"first_name":"Charles","last_name":"Nwaozuzu","domain_name":"mun","page_name":"CharlesNwaozuzu","display_name":"Charles Nwaozuzu","profile_url":"https://mun.academia.edu/CharlesNwaozuzu?f_ri=22613","photo":"https://0.academia-photos.com/23436368/6355550/7214030/s65_charles.nwaozuzu.jpg"}</script></span></span></li><li class="js-paper-rank-work_9746576 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="9746576"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 9746576, container: ".js-paper-rank-work_9746576", }); });</script></li><li class="js-percentile-work_9746576 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 9746576; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_9746576"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_9746576 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="9746576"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 9746576; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=9746576]").text(description); $(".js-view-count-work_9746576").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_9746576").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="9746576"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i></div><span class="InlineList-item-text u-textTruncate u-pl6x"><a class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a><script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (false) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=9746576]'), work: {"id":9746576,"title":"Application of Probabilistic Methods to Predict Derby Matches in the English Premier League (EPL)","created_at":"2014-12-12T15:58:02.870-08:00","url":"https://www.academia.edu/9746576/Application_of_Probabilistic_Methods_to_Predict_Derby_Matches_in_the_English_Premier_League_EPL_?f_ri=22613","dom_id":"work_9746576","summary":"The Premier League is being broadcasted in 212 territories around the world, working with 80 different broadcasters. In addition, football is the world’s most popular game and constitutes the fastest growing gambling market. As a result, researchers continue to introduce a variety of football models which are formulated by different forecast methodologies. In this report, we attempt to predict match outcomes of some famous derbies being played in EPL from December 2014 to May 2015. We present a Poisson distribution model for forecasting these matches using data from 2013-14 season till 24th November of 2014-15 season. Using this model we obtain 1) probability of who might win the game, 2) probability of different outcomes (number of goals scored by each team) and 3) if home advantage favours the home team. This model is an introduction to the predicting individual games can be developed further into a betting model to predict the season results and individual scores.","downloadable_attachments":[{"id":35929614,"asset_id":9746576,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":23436368,"first_name":"Charles","last_name":"Nwaozuzu","domain_name":"mun","page_name":"CharlesNwaozuzu","display_name":"Charles Nwaozuzu","profile_url":"https://mun.academia.edu/CharlesNwaozuzu?f_ri=22613","photo":"https://0.academia-photos.com/23436368/6355550/7214030/s65_charles.nwaozuzu.jpg"}],"research_interests":[{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_13244418" data-work_id="13244418" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/13244418/An%C3%A1lisis_de_regresi%C3%B3n_correlaci%C3%B3n_lineal_y_distribuciones_de_probabilidad">Análisis de regresión, correlación lineal y distribuciones de probabilidad</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/13244418" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ffc585f26c6c690303ed90d714989417" rel="nofollow" data-download="{"attachment_id":37992934,"asset_id":13244418,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/37992934/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="32500359" href="https://ieuonline.academia.edu/FabyMingo">Faby Mingo</a><script data-card-contents-for-user="32500359" type="text/json">{"id":32500359,"first_name":"Faby","last_name":"Mingo","domain_name":"ieuonline","page_name":"FabyMingo","display_name":"Faby Mingo","profile_url":"https://ieuonline.academia.edu/FabyMingo?f_ri=22613","photo":"https://0.academia-photos.com/32500359/9718932/10825634/s65_faby.mingo.jpg_oh_559de958ac63dbe467091b66c758d7da_oe_5624ed5d___gda___1441233527_44cff2f8b7bb40da8ba7a292a3134c03"}</script></span></span></li><li class="js-paper-rank-work_13244418 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="13244418"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 13244418, container: ".js-paper-rank-work_13244418", }); });</script></li><li class="js-percentile-work_13244418 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 13244418; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_13244418"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_13244418 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="13244418"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13244418; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13244418]").text(description); $(".js-view-count-work_13244418").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_13244418").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="13244418"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i></div><span class="InlineList-item-text u-textTruncate u-pl6x"><a class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a><script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (false) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=13244418]'), work: {"id":13244418,"title":"Análisis de regresión, correlación lineal y distribuciones de probabilidad","created_at":"2015-06-24T08:23:56.759-07:00","url":"https://www.academia.edu/13244418/An%C3%A1lisis_de_regresi%C3%B3n_correlaci%C3%B3n_lineal_y_distribuciones_de_probabilidad?f_ri=22613","dom_id":"work_13244418","summary":null,"downloadable_attachments":[{"id":37992934,"asset_id":13244418,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":32500359,"first_name":"Faby","last_name":"Mingo","domain_name":"ieuonline","page_name":"FabyMingo","display_name":"Faby Mingo","profile_url":"https://ieuonline.academia.edu/FabyMingo?f_ri=22613","photo":"https://0.academia-photos.com/32500359/9718932/10825634/s65_faby.mingo.jpg_oh_559de958ac63dbe467091b66c758d7da_oe_5624ed5d___gda___1441233527_44cff2f8b7bb40da8ba7a292a3134c03"}],"research_interests":[{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_35454045" data-work_id="35454045" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/35454045/The_mathematical_expectation_reflexion_and_existence_as_numerical_means_of_a_co_event_in_the_theory_of_experience_and_chance">The mathematical expectation, reflexion, and existence as numerical means of a co~event in the theory of experience and chance</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The theory of dual numerical means of random and experienced variables is briefly described in the framework of the new theory of experience and the chance that arises as an axiomatic synthesis of two dual theories — the Kolmogorov theory... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_35454045" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The theory of dual numerical means of random and experienced variables is briefly described in the framework of the new theory of experience and the chance that arises as an axiomatic synthesis of two dual theories — the Kolmogorov theory of probability and the theory of believability. A new term is introduced for the numerical mean of the experienced variable — mathematical reflection, which is dual to the mathematical expectation of a random variable within the framework of the new theory. The basic properties and examples of dual numerical means are considered.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/35454045" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c987fbbcba82b22244a9e6134aee09b0" rel="nofollow" data-download="{"attachment_id":58563667,"asset_id":35454045,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/58563667/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="38890" href="https://sfu-kras.academia.edu/OlegVorobyev">Oleg Yu Vorobyev</a><script data-card-contents-for-user="38890" type="text/json">{"id":38890,"first_name":"Oleg Yu","last_name":"Vorobyev","domain_name":"sfu-kras","page_name":"OlegVorobyev","display_name":"Oleg Yu Vorobyev","profile_url":"https://sfu-kras.academia.edu/OlegVorobyev?f_ri=22613","photo":"https://0.academia-photos.com/38890/12977/347919/s65_oleg.vorobyev.gif"}</script></span></span></li><li class="js-paper-rank-work_35454045 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="35454045"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 35454045, container: ".js-paper-rank-work_35454045", }); 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A new term is introduced for the numerical mean of the experienced variable — mathematical reflection, which is dual to the mathematical expectation of a random variable within the framework of the new theory. 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u-pv7x u-mb0x js-work-card work_18454596" data-work_id="18454596" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/18454596/Geometric_Mean_for_Subspace_Selection">Geometric Mean for Subspace Selection</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Subspace selection approaches are powerful tools in pattern classification and data visualization. One of the most important subspace approaches is the linear dimensionality reduction step in the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_18454596" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Subspace selection approaches are powerful tools in pattern classification and data visualization. One of the most important subspace approaches is the linear dimensionality reduction step in the Fisher&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s linear discriminant analysis (FLDA), which has been successfully employed in many fields such as biometrics, bioinformatics, and multimedia information management. However, the linear dimensionality reduction step in FLDA has a critical drawback: for a classification task with c classes, if the dimension of the projected subspace is strictly lower than c - 1, the projection to a subspace tends to merge those classes, which are close together in the original feature space. If separate classes are sampled from Gaussian distributions, all with identical covariance matrices, then the linear dimensionality reduction step in FLDA maximizes the mean value of the Kullback-Leibler (KL) divergences between different classes. Based on this viewpoint, the geometric mean for subspace selection is studied in this paper. Three criteria are analyzed: 1) maximization of the geometric mean of the KL divergences, 2) maximization of the geometric mean of the normalized KL divergences, and 3) the combination of 1 and 2. Preliminary experimental results based on synthetic data, UCI Machine Learning Repository, and handwriting digits show that the third criterion is a potential discriminative subspace selection method, which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in FLDA and its several representative extensions.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/18454596" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e2ad8465dce1620ce8d68f7530eb32c0" rel="nofollow" data-download="{"attachment_id":42167986,"asset_id":18454596,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/42167986/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="38463668" href="https://independent.academia.edu/StephenMaybank">Stephen Maybank</a><script data-card-contents-for-user="38463668" type="text/json">{"id":38463668,"first_name":"Stephen","last_name":"Maybank","domain_name":"independent","page_name":"StephenMaybank","display_name":"Stephen Maybank","profile_url":"https://independent.academia.edu/StephenMaybank?f_ri=22613","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_18454596 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="18454596"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 18454596, container: ".js-paper-rank-work_18454596", }); 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One of the most important subspace approaches is the linear dimensionality reduction step in the Fisher\u0026amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s linear discriminant analysis (FLDA), which has been successfully employed in many fields such as biometrics, bioinformatics, and multimedia information management. However, the linear dimensionality reduction step in FLDA has a critical drawback: for a classification task with c classes, if the dimension of the projected subspace is strictly lower than c - 1, the projection to a subspace tends to merge those classes, which are close together in the original feature space. If separate classes are sampled from Gaussian distributions, all with identical covariance matrices, then the linear dimensionality reduction step in FLDA maximizes the mean value of the Kullback-Leibler (KL) divergences between different classes. Based on this viewpoint, the geometric mean for subspace selection is studied in this paper. Three criteria are analyzed: 1) maximization of the geometric mean of the KL divergences, 2) maximization of the geometric mean of the normalized KL divergences, and 3) the combination of 1 and 2. Preliminary experimental results based on synthetic data, UCI Machine Learning Repository, and handwriting digits show that the third criterion is a potential discriminative subspace selection method, which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in FLDA and its several representative extensions.","downloadable_attachments":[{"id":42167986,"asset_id":18454596,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":38463668,"first_name":"Stephen","last_name":"Maybank","domain_name":"independent","page_name":"StephenMaybank","display_name":"Stephen Maybank","profile_url":"https://independent.academia.edu/StephenMaybank?f_ri=22613","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":37,"name":"Information 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class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a>, <script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="571797" href="https://www.academia.edu/Documents/in/Introduction_to_Probability">Introduction to Probability</a><script data-card-contents-for-ri="571797" type="text/json">{"id":571797,"name":"Introduction to Probability","url":"https://www.academia.edu/Documents/in/Introduction_to_Probability?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=6993713]'), work: {"id":6993713,"title":"Understanding and Calculating the Odds: Probability Theory Basics and Calculus Guide for Beginners, with Applications in Games of Chance and Everyday Life","created_at":"2014-05-08T17:33:00.540-07:00","url":"https://www.academia.edu/6993713/Understanding_and_Calculating_the_Odds_Probability_Theory_Basics_and_Calculus_Guide_for_Beginners_with_Applications_in_Games_of_Chance_and_Everyday_Life?f_ri=22613","dom_id":"work_6993713","summary":null,"downloadable_attachments":[{"id":43348348,"asset_id":6993713,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":11871659,"first_name":"Catalin","last_name":"Barboianu","domain_name":"unibuc","page_name":"CatalinBarboianu","display_name":"Catalin Barboianu","profile_url":"https://unibuc.academia.edu/CatalinBarboianu?f_ri=22613","photo":"https://0.academia-photos.com/11871659/3419807/4021785/s65_catalin.barboianu.jpg"}],"research_interests":[{"id":344,"name":"Probability Theory","url":"https://www.academia.edu/Documents/in/Probability_Theory?f_ri=22613","nofollow":false},{"id":2731,"name":"Mathematics Education","url":"https://www.academia.edu/Documents/in/Mathematics_Education?f_ri=22613","nofollow":false},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false},{"id":571797,"name":"Introduction to Probability","url":"https://www.academia.edu/Documents/in/Introduction_to_Probability?f_ri=22613","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_54478101" data-work_id="54478101" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/54478101/On_the_probabilistic_proof_of_the_convergence_of_the_Collatz_conjecture">On the probabilistic proof of the convergence of the Collatz conjecture</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A new approach towards probabilistic proof of the convergence of the Collatz conjecture is described via identifying a sequential correlation of even natural numbers by divisions by 2 that follows a recurrent pattern of the form 𝑥, 1, 𝑥,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_54478101" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A new approach towards probabilistic proof of the convergence of the Collatz conjecture is described via identifying a sequential correlation of even natural numbers by divisions by 2 that follows a recurrent pattern of the form 𝑥, 1, 𝑥, 1 . . ., where 𝑥 represents divisions by 2 more than once.The sequence presents a probability of 50:50 of division by 2 more than once as opposed to division by 2 once over the even natural numbers.The sequence also gives the same 50:50 probability of consecutive Collatz even elements when counted for division by 2 more than once as opposed to division by 2 once and a ratio of 3:1. Considering Collatz function producing random numbers and over sufficient number of iterations, this probability distribution produces numbers in descending order that lead to the convergence of the Collatz function to 1, assuming that the only cycle of the function is 1-4-2-1.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/54478101" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e28235e42dc4ef8d2abb8ab07c4c648a" rel="nofollow" data-download="{"attachment_id":70821740,"asset_id":54478101,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/70821740/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="121682377" href="https://wwwpmu.academia.edu/KamalBarghout">Kamal Barghout</a><script data-card-contents-for-user="121682377" type="text/json">{"id":121682377,"first_name":"Kamal","last_name":"Barghout","domain_name":"wwwpmu","page_name":"KamalBarghout","display_name":"Kamal Barghout","profile_url":"https://wwwpmu.academia.edu/KamalBarghout?f_ri=22613","photo":"https://0.academia-photos.com/121682377/30343801/28133742/s65_kamal.barghout.jpg"}</script></span></span></li><li class="js-paper-rank-work_54478101 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="54478101"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 54478101, container: ".js-paper-rank-work_54478101", }); 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$(".js-view-count[data-work-id=54478101]").text(description); $(".js-view-count-work_54478101").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_54478101").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="54478101"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="300" href="https://www.academia.edu/Documents/in/Mathematics">Mathematics</a>, <script data-card-contents-for-ri="300" type="text/json">{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="301" href="https://www.academia.edu/Documents/in/Number_Theory">Number Theory</a>, <script data-card-contents-for-ri="301" type="text/json">{"id":301,"name":"Number Theory","url":"https://www.academia.edu/Documents/in/Number_Theory?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="892" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a><script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=54478101]'), work: {"id":54478101,"title":"On the probabilistic proof of the convergence of the Collatz conjecture","created_at":"2021-09-30T18:59:29.139-07:00","url":"https://www.academia.edu/54478101/On_the_probabilistic_proof_of_the_convergence_of_the_Collatz_conjecture?f_ri=22613","dom_id":"work_54478101","summary":"A new approach towards probabilistic proof of the convergence of the Collatz conjecture is described via identifying a sequential correlation of even natural numbers by divisions by 2 that follows a recurrent pattern of the form 𝑥, 1, 𝑥, 1 . . ., where 𝑥 represents divisions by 2 more than once.The sequence presents a probability of 50:50 of division by 2 more than once as opposed to division by 2 once over the even natural numbers.The sequence also gives the same 50:50 probability of consecutive Collatz even elements when counted for division by 2 more than once as opposed to division by 2 once and a ratio of 3:1. Considering Collatz function producing random numbers and over sufficient number of iterations, this probability distribution produces numbers in descending order that lead to the convergence of the Collatz function to 1, assuming that the only cycle of the function is 1-4-2-1.","downloadable_attachments":[{"id":70821740,"asset_id":54478101,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":121682377,"first_name":"Kamal","last_name":"Barghout","domain_name":"wwwpmu","page_name":"KamalBarghout","display_name":"Kamal Barghout","profile_url":"https://wwwpmu.academia.edu/KamalBarghout?f_ri=22613","photo":"https://0.academia-photos.com/121682377/30343801/28133742/s65_kamal.barghout.jpg"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=22613","nofollow":false},{"id":301,"name":"Number Theory","url":"https://www.academia.edu/Documents/in/Number_Theory?f_ri=22613","nofollow":false},{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=22613","nofollow":false},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false},{"id":29082,"name":"Sequence Analysis","url":"https://www.academia.edu/Documents/in/Sequence_Analysis?f_ri=22613"},{"id":33069,"name":"Probability","url":"https://www.academia.edu/Documents/in/Probability?f_ri=22613"},{"id":81504,"name":"Correlation","url":"https://www.academia.edu/Documents/in/Correlation?f_ri=22613"},{"id":388873,"name":"Mathematics and Statistics","url":"https://www.academia.edu/Documents/in/Mathematics_and_Statistics?f_ri=22613"},{"id":494153,"name":"Collatz Conjecture","url":"https://www.academia.edu/Documents/in/Collatz_Conjecture?f_ri=22613"},{"id":1184493,"name":"Conjecture and Proof","url":"https://www.academia.edu/Documents/in/Conjecture_and_Proof?f_ri=22613"},{"id":1310920,"name":"Syracuse Conjecture","url":"https://www.academia.edu/Documents/in/Syracuse_Conjecture?f_ri=22613"},{"id":1523776,"name":"Probability statistics","url":"https://www.academia.edu/Documents/in/Probability_statistics?f_ri=22613"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_79085920" data-work_id="79085920" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/79085920/Harmonic_summation_and_assessment_based_on_probability_distribution_of_phase_angle">Harmonic summation and assessment based on probability distribution of phase angle</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">At the point of common coupling (PCC), the summation of two harmonic voltage vectors at same frequency is only certain if their amplitudes and phase angles are well known. Therefore, there are many cases where the phase angle difference... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_79085920" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">At the point of common coupling (PCC), the summation of two harmonic voltage vectors at same frequency is only certain if their amplitudes and phase angles are well known. Therefore, there are many cases where the phase angle difference between harmonic voltage vectors is unknown. In practice, for example, in calculating the steady state rating of the a.c. passive harmonic filter, arithmetic summation method is often used in determining maximal harmonic level. This summation law is very conservative as it may result in high cost of equipment ratings. Actually, a compromise should be made in taking into account the equipment safety and the risk of overrating as well as excessive costs. Based on uniform distribution of difference in phase angles, the paper provides an algorithm to calculate the probability upon which the magnitude of summation vector of two harmonic voltages may exceed a given value. On the basis of the probability and the given harmonic value, harmonic summation can be assessed. This may also result in a recommendation of another harmonic summation method for the standard IEC/TR 61000-3-6.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/79085920" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="179619951" href="https://independent.academia.edu/XavierLimZhengYang">Xavier Lim Zheng Yang</a><script data-card-contents-for-user="179619951" type="text/json">{"id":179619951,"first_name":"Xavier Lim","last_name":"Zheng Yang","domain_name":"independent","page_name":"XavierLimZhengYang","display_name":"Xavier Lim Zheng Yang","profile_url":"https://independent.academia.edu/XavierLimZhengYang?f_ri=22613","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_79085920 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="79085920"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 79085920, container: ".js-paper-rank-work_79085920", }); });</script></li><li class="js-percentile-work_79085920 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79085920; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_79085920"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_79085920 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="79085920"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79085920; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79085920]").text(description); $(".js-view-count-work_79085920").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_79085920").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="79085920"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">14</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="374" href="https://www.academia.edu/Documents/in/Harmonic_Analysis">Harmonic Analysis</a>, <script data-card-contents-for-ri="374" type="text/json">{"id":374,"name":"Harmonic Analysis","url":"https://www.academia.edu/Documents/in/Harmonic_Analysis?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="1424" href="https://www.academia.edu/Documents/in/Power_Systems">Power Systems</a>, <script data-card-contents-for-ri="1424" type="text/json">{"id":1424,"name":"Power Systems","url":"https://www.academia.edu/Documents/in/Power_Systems?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="5560" href="https://www.academia.edu/Documents/in/Electromagnetic_Compatibility">Electromagnetic Compatibility</a>, <script data-card-contents-for-ri="5560" type="text/json">{"id":5560,"name":"Electromagnetic Compatibility","url":"https://www.academia.edu/Documents/in/Electromagnetic_Compatibility?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a><script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=79085920]'), work: {"id":79085920,"title":"Harmonic summation and assessment based on probability distribution of phase angle","created_at":"2022-05-13T14:59:58.892-07:00","url":"https://www.academia.edu/79085920/Harmonic_summation_and_assessment_based_on_probability_distribution_of_phase_angle?f_ri=22613","dom_id":"work_79085920","summary":"At the point of common coupling (PCC), the summation of two harmonic voltage vectors at same frequency is only certain if their amplitudes and phase angles are well known. Therefore, there are many cases where the phase angle difference between harmonic voltage vectors is unknown. In practice, for example, in calculating the steady state rating of the a.c. passive harmonic filter, arithmetic summation method is often used in determining maximal harmonic level. This summation law is very conservative as it may result in high cost of equipment ratings. Actually, a compromise should be made in taking into account the equipment safety and the risk of overrating as well as excessive costs. Based on uniform distribution of difference in phase angles, the paper provides an algorithm to calculate the probability upon which the magnitude of summation vector of two harmonic voltages may exceed a given value. On the basis of the probability and the given harmonic value, harmonic summation can be assessed. This may also result in a recommendation of another harmonic summation method for the standard IEC/TR 61000-3-6.","downloadable_attachments":[],"ordered_authors":[{"id":179619951,"first_name":"Xavier Lim","last_name":"Zheng Yang","domain_name":"independent","page_name":"XavierLimZhengYang","display_name":"Xavier Lim Zheng Yang","profile_url":"https://independent.academia.edu/XavierLimZhengYang?f_ri=22613","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":374,"name":"Harmonic Analysis","url":"https://www.academia.edu/Documents/in/Harmonic_Analysis?f_ri=22613","nofollow":false},{"id":1424,"name":"Power Systems","url":"https://www.academia.edu/Documents/in/Power_Systems?f_ri=22613","nofollow":false},{"id":5560,"name":"Electromagnetic Compatibility","url":"https://www.academia.edu/Documents/in/Electromagnetic_Compatibility?f_ri=22613","nofollow":false},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false},{"id":50711,"name":"Risk Analysis","url":"https://www.academia.edu/Documents/in/Risk_Analysis?f_ri=22613"},{"id":234860,"name":"Steady state","url":"https://www.academia.edu/Documents/in/Steady_state?f_ri=22613"},{"id":383728,"name":"Vectors","url":"https://www.academia.edu/Documents/in/Vectors?f_ri=22613"},{"id":539877,"name":"Pulse Width Modulation","url":"https://www.academia.edu/Documents/in/Pulse_Width_Modulation?f_ri=22613"},{"id":825908,"name":"Harmonics","url":"https://www.academia.edu/Documents/in/Harmonics?f_ri=22613"},{"id":1169428,"name":"Power System Harmonics","url":"https://www.academia.edu/Documents/in/Power_System_Harmonics?f_ri=22613"},{"id":1952679,"name":"Statistical Distributions","url":"https://www.academia.edu/Documents/in/Statistical_Distributions?f_ri=22613"},{"id":2814568,"name":"Probability Distribution","url":"https://www.academia.edu/Documents/in/Probability_Distribution?f_ri=22613"},{"id":2960537,"name":"Phase Angle","url":"https://www.academia.edu/Documents/in/Phase_Angle?f_ri=22613"},{"id":3648106,"name":"uniform distribution","url":"https://www.academia.edu/Documents/in/uniform_distribution?f_ri=22613"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_78697271" data-work_id="78697271" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/78697271/Is_postgraduate_English_academic_writing_more_clausal_or_phrasal_Syntactic_complexification_at_the_crossroads_of_genre_proficiency_and_statistical_modelling">Is postgraduate English academic writing more clausal or phrasal? Syntactic complexification at the crossroads of genre, proficiency, and statistical modelling</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Abstract This study uses a novel approach to describe modern English academic texts in terms of the amount and distribution of syntactic subordination, coordination and phrasal structures. Inconsistent results have been reported in... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_78697271" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Abstract This study uses a novel approach to describe modern English academic texts in terms of the amount and distribution of syntactic subordination, coordination and phrasal structures. Inconsistent results have been reported in previous scholarship regarding the trajectory of syntactic complexification based on English language backgrounds and linguistic proficiency of writers. For the first time, a combination of two predictive statistical modelling methods of mixed-effects modelling and supervised machine learning modelling with random forest is used to examine the extent to which the type, amount, and distribution of these syntactic structures can be attributed to a text-intrinsic feature (sub-genres or rhetorical sections of academic register) and a text-extrinsic factor (English language background based on academic context) in a corpus of master’s dissertations. To revisit the theories of English L1 vs. L2 texts, strong predictors of syntactic complexity across academic writings of EFL, ESL, and English L1 postgraduate students were identified. The findings show that EFL texts are dominantly subordinate in nature, English L1’s dominantly phrasal, and the ESL dissertations exhibited similar amounts of subordination and phrasal structures. Distinct syntactic structures also characterise rhetorical sections: abstracts are predominantly phrasal, literature reviews distinctly subordinate, and conclusion sections have noticeable amounts of verb phrases.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/78697271" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="153920732" href="https://bham.academia.edu/mnasseri">maryam nasseri</a><script data-card-contents-for-user="153920732" type="text/json">{"id":153920732,"first_name":"maryam","last_name":"nasseri","domain_name":"bham","page_name":"mnasseri","display_name":"maryam nasseri","profile_url":"https://bham.academia.edu/mnasseri?f_ri=22613","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_78697271 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="78697271"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 78697271, container: ".js-paper-rank-work_78697271", }); });</script></li><li class="js-percentile-work_78697271 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78697271; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_78697271"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_78697271 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="78697271"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78697271; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78697271]").text(description); $(".js-view-count-work_78697271").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_78697271").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="78697271"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">14</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="1006" href="https://www.academia.edu/Documents/in/English_for_Academic_Purposes">English for Academic Purposes</a>, <script data-card-contents-for-ri="1006" type="text/json">{"id":1006,"name":"English for Academic Purposes","url":"https://www.academia.edu/Documents/in/English_for_Academic_Purposes?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="1007" href="https://www.academia.edu/Documents/in/Teaching_English_as_a_Second_Language">Teaching English as a Second Language</a>, <script data-card-contents-for-ri="1007" type="text/json">{"id":1007,"name":"Teaching English as a Second Language","url":"https://www.academia.edu/Documents/in/Teaching_English_as_a_Second_Language?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="3979" href="https://www.academia.edu/Documents/in/English_language">English language</a>, <script data-card-contents-for-ri="3979" type="text/json">{"id":3979,"name":"English language","url":"https://www.academia.edu/Documents/in/English_language?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4738" href="https://www.academia.edu/Documents/in/Academic_Writing">Academic Writing</a><script data-card-contents-for-ri="4738" type="text/json">{"id":4738,"name":"Academic Writing","url":"https://www.academia.edu/Documents/in/Academic_Writing?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=78697271]'), work: {"id":78697271,"title":"Is postgraduate English academic writing more clausal or phrasal? Syntactic complexification at the crossroads of genre, proficiency, and statistical modelling","created_at":"2022-05-07T12:22:26.644-07:00","url":"https://www.academia.edu/78697271/Is_postgraduate_English_academic_writing_more_clausal_or_phrasal_Syntactic_complexification_at_the_crossroads_of_genre_proficiency_and_statistical_modelling?f_ri=22613","dom_id":"work_78697271","summary":"Abstract This study uses a novel approach to describe modern English academic texts in terms of the amount and distribution of syntactic subordination, coordination and phrasal structures. Inconsistent results have been reported in previous scholarship regarding the trajectory of syntactic complexification based on English language backgrounds and linguistic proficiency of writers. For the first time, a combination of two predictive statistical modelling methods of mixed-effects modelling and supervised machine learning modelling with random forest is used to examine the extent to which the type, amount, and distribution of these syntactic structures can be attributed to a text-intrinsic feature (sub-genres or rhetorical sections of academic register) and a text-extrinsic factor (English language background based on academic context) in a corpus of master’s dissertations. To revisit the theories of English L1 vs. L2 texts, strong predictors of syntactic complexity across academic writings of EFL, ESL, and English L1 postgraduate students were identified. The findings show that EFL texts are dominantly subordinate in nature, English L1’s dominantly phrasal, and the ESL dissertations exhibited similar amounts of subordination and phrasal structures. Distinct syntactic structures also characterise rhetorical sections: abstracts are predominantly phrasal, literature reviews distinctly subordinate, and conclusion sections have noticeable amounts of verb phrases.","downloadable_attachments":[],"ordered_authors":[{"id":153920732,"first_name":"maryam","last_name":"nasseri","domain_name":"bham","page_name":"mnasseri","display_name":"maryam nasseri","profile_url":"https://bham.academia.edu/mnasseri?f_ri=22613","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1006,"name":"English for Academic Purposes","url":"https://www.academia.edu/Documents/in/English_for_Academic_Purposes?f_ri=22613","nofollow":false},{"id":1007,"name":"Teaching English as a Second Language","url":"https://www.academia.edu/Documents/in/Teaching_English_as_a_Second_Language?f_ri=22613","nofollow":false},{"id":3979,"name":"English language","url":"https://www.academia.edu/Documents/in/English_language?f_ri=22613","nofollow":false},{"id":4738,"name":"Academic Writing","url":"https://www.academia.edu/Documents/in/Academic_Writing?f_ri=22613","nofollow":false},{"id":7709,"name":"Applied Linguistics","url":"https://www.academia.edu/Documents/in/Applied_Linguistics?f_ri=22613"},{"id":13297,"name":"Cognitive Linguistics","url":"https://www.academia.edu/Documents/in/Cognitive_Linguistics?f_ri=22613"},{"id":14585,"name":"Statistical Modeling","url":"https://www.academia.edu/Documents/in/Statistical_Modeling?f_ri=22613"},{"id":15674,"name":"Linguistics","url":"https://www.academia.edu/Documents/in/Linguistics?f_ri=22613"},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613"},{"id":27324,"name":"R programming language","url":"https://www.academia.edu/Documents/in/R_programming_language?f_ri=22613"},{"id":75516,"name":"Syntactic Complexity","url":"https://www.academia.edu/Documents/in/Syntactic_Complexity?f_ri=22613"},{"id":158331,"name":"Python Programming","url":"https://www.academia.edu/Documents/in/Python_Programming?f_ri=22613"},{"id":184145,"name":"TESL/TEFL","url":"https://www.academia.edu/Documents/in/TESL_TEFL?f_ri=22613"},{"id":624707,"name":"English Language","url":"https://www.academia.edu/Documents/in/English_Language-2?f_ri=22613"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_11271166" data-work_id="11271166" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/11271166/PROBABILITY_AND_STATISTICS_FOR_ENGINEER">PROBABILITY AND STATISTICS FOR ENGINEER</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Why Study Statistics? Modern Statistics Statistics and Engineering The Role of the Scientist and Engineer in Quality Improvement A Case Study : Visually Inspecting Data to Improve Product Quality Two Basic Concepts - Population and Sample... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_11271166" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Why Study Statistics?<br />Modern Statistics<br />Statistics and Engineering<br />The Role of the Scientist and Engineer in Quality Improvement<br />A Case Study : Visually Inspecting Data to Improve Product Quality<br />Two Basic Concepts - Population and Sample<br />Treatment of Data<br />Pareto Diagrams and Dot Diagrams<br />Frequency Distributions<br />Graphs of Frequency Distributions<br />Stem-and-leaf Displays<br />Descriptive Measures<br />Quartiles and Percentiles<br />The Calculation of x and s<br />A Case Study: Problems with Aggregating<br />Data</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/11271166" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ef7ba0c4399d4e8dd507912f86bae2d2" rel="nofollow" data-download="{"attachment_id":36858553,"asset_id":11271166,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/36858553/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="5918344" href="https://umrah.academia.edu/PujaSetiawan">Puja Setiawan</a><script data-card-contents-for-user="5918344" type="text/json">{"id":5918344,"first_name":"Puja","last_name":"Setiawan","domain_name":"umrah","page_name":"PujaSetiawan","display_name":"Puja Setiawan","profile_url":"https://umrah.academia.edu/PujaSetiawan?f_ri=22613","photo":"https://0.academia-photos.com/5918344/2521096/19228639/s65_puja.setiawan.jpg"}</script></span></span></li><li class="js-paper-rank-work_11271166 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="11271166"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 11271166, container: ".js-paper-rank-work_11271166", }); });</script></li><li class="js-percentile-work_11271166 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 11271166; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_11271166"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_11271166 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="11271166"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 11271166; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=11271166]").text(description); $(".js-view-count-work_11271166").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_11271166").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="11271166"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">17</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="892" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="1352" href="https://www.academia.edu/Documents/in/Multivariate_Statistics">Multivariate Statistics</a>, <script data-card-contents-for-ri="1352" type="text/json">{"id":1352,"name":"Multivariate Statistics","url":"https://www.academia.edu/Documents/in/Multivariate_Statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2606" href="https://www.academia.edu/Documents/in/Innovation_statistics">Innovation statistics</a>, <script data-card-contents-for-ri="2606" type="text/json">{"id":2606,"name":"Innovation statistics","url":"https://www.academia.edu/Documents/in/Innovation_statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4060" href="https://www.academia.edu/Documents/in/Applied_Statistics">Applied Statistics</a><script data-card-contents-for-ri="4060" type="text/json">{"id":4060,"name":"Applied Statistics","url":"https://www.academia.edu/Documents/in/Applied_Statistics?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=11271166]'), work: {"id":11271166,"title":"PROBABILITY AND STATISTICS FOR ENGINEER","created_at":"2015-03-04T11:11:45.758-08:00","url":"https://www.academia.edu/11271166/PROBABILITY_AND_STATISTICS_FOR_ENGINEER?f_ri=22613","dom_id":"work_11271166","summary":"Why Study Statistics?\nModern Statistics\nStatistics and Engineering\nThe Role of the Scientist and Engineer in Quality Improvement\nA Case Study : Visually Inspecting Data to Improve Product Quality\nTwo Basic Concepts - Population and Sample\nTreatment of Data\nPareto Diagrams and Dot Diagrams\nFrequency Distributions\nGraphs of Frequency Distributions\nStem-and-leaf Displays\nDescriptive Measures\nQuartiles and Percentiles\nThe Calculation of x and s\nA Case Study: Problems with Aggregating\nData\n","downloadable_attachments":[{"id":36858553,"asset_id":11271166,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":5918344,"first_name":"Puja","last_name":"Setiawan","domain_name":"umrah","page_name":"PujaSetiawan","display_name":"Puja Setiawan","profile_url":"https://umrah.academia.edu/PujaSetiawan?f_ri=22613","photo":"https://0.academia-photos.com/5918344/2521096/19228639/s65_puja.setiawan.jpg"}],"research_interests":[{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=22613","nofollow":false},{"id":1352,"name":"Multivariate Statistics","url":"https://www.academia.edu/Documents/in/Multivariate_Statistics?f_ri=22613","nofollow":false},{"id":2606,"name":"Innovation statistics","url":"https://www.academia.edu/Documents/in/Innovation_statistics?f_ri=22613","nofollow":false},{"id":4060,"name":"Applied Statistics","url":"https://www.academia.edu/Documents/in/Applied_Statistics?f_ri=22613","nofollow":false},{"id":4388,"name":"Computational Statistics","url":"https://www.academia.edu/Documents/in/Computational_Statistics?f_ri=22613"},{"id":4840,"name":"Statistics Education","url":"https://www.academia.edu/Documents/in/Statistics_Education?f_ri=22613"},{"id":15198,"name":"Spatial Statistics","url":"https://www.academia.edu/Documents/in/Spatial_Statistics?f_ri=22613"},{"id":17183,"name":"Mathematical Psychology and Statistics","url":"https://www.academia.edu/Documents/in/Mathematical_Psychology_and_Statistics?f_ri=22613"},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613"},{"id":31412,"name":"Probability and Mathematical Statistics","url":"https://www.academia.edu/Documents/in/Probability_and_Mathematical_Statistics?f_ri=22613"},{"id":41239,"name":"Bayesian statistics \u0026 modelling","url":"https://www.academia.edu/Documents/in/Bayesian_statistics_and_modelling?f_ri=22613"},{"id":53779,"name":"statistics with SPSS and Excel","url":"https://www.academia.edu/Documents/in/statistics_with_SPSS_and_Excel?f_ri=22613"},{"id":388873,"name":"Mathematics and Statistics","url":"https://www.academia.edu/Documents/in/Mathematics_and_Statistics?f_ri=22613"},{"id":579838,"name":"Experimental Statistics: Statistical Analysis of Experiments","url":"https://www.academia.edu/Documents/in/Experimental_Statistics_Statistical_Analysis_of_Experiments?f_ri=22613"},{"id":808849,"name":"Frequency Distributions and Time Distributions of Wind Speed","url":"https://www.academia.edu/Documents/in/Frequency_Distributions_and_Time_Distributions_of_Wind_Speed?f_ri=22613"},{"id":1838542,"name":"Modern Statistics","url":"https://www.academia.edu/Documents/in/Modern_Statistics?f_ri=22613"},{"id":1838547,"name":"Treatment of Data","url":"https://www.academia.edu/Documents/in/Treatment_of_Data?f_ri=22613"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_37503621" data-work_id="37503621" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/37503621/THE_MACKEY_GLASS_TYPE_DELAY_DIFFERENTIAL_EQUATION_WITH_UNIFORMLY_GENERATED_CONSTANTS">THE MACKEY-GLASS TYPE DELAY DIFFERENTIAL EQUATION WITH UNIFORMLY GENERATED CONSTANTS</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A method for computing Mackey-Glass equation of the form   () (), () y t f y t y t    is examined in this paper. For a given lag  , uniformly generated and fixed beta is observed for estimating the y  (density  time-units in the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_37503621" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A method for computing Mackey-Glass equation of the form   () (), () y t f y t y t    is examined in this paper. For a given lag  , uniformly generated and fixed beta is observed for estimating the y  (density  time-units in the past). The technique was applied to blood destruction and production in human body.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/37503621" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="638188a364584a1b6f58016a8037f625" rel="nofollow" data-download="{"attachment_id":57475033,"asset_id":37503621,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/57475033/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3522766" href="https://run.academia.edu/DrOLUMIDEADESINA">Dr. OLUMIDE S ADESINA</a><script data-card-contents-for-user="3522766" type="text/json">{"id":3522766,"first_name":"Dr. OLUMIDE","last_name":"ADESINA","domain_name":"run","page_name":"DrOLUMIDEADESINA","display_name":"Dr. OLUMIDE S ADESINA","profile_url":"https://run.academia.edu/DrOLUMIDEADESINA?f_ri=22613","photo":"https://0.academia-photos.com/3522766/6189170/24702925/s65_dr._olumide.adesina.jpg"}</script></span></span></li><li class="js-paper-rank-work_37503621 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="37503621"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 37503621, container: ".js-paper-rank-work_37503621", }); 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For a given lag  , uniformly generated and fixed beta is observed for estimating the y  (density  time-units in the past). The technique was applied to blood destruction and production in human body.","downloadable_attachments":[{"id":57475033,"asset_id":37503621,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":3522766,"first_name":"Dr. OLUMIDE","last_name":"ADESINA","domain_name":"run","page_name":"DrOLUMIDEADESINA","display_name":"Dr. OLUMIDE S ADESINA","profile_url":"https://run.academia.edu/DrOLUMIDEADESINA?f_ri=22613","photo":"https://0.academia-photos.com/3522766/6189170/24702925/s65_dr._olumide.adesina.jpg"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=22613","nofollow":false},{"id":307,"name":"Mathematical Statistics","url":"https://www.academia.edu/Documents/in/Mathematical_Statistics?f_ri=22613","nofollow":false},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false},{"id":30888,"name":"Differential 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href="https://www.academia.edu/Documents/in/Probability_Theory">Probability Theory</a>, <script data-card-contents-for-ri="344" type="text/json">{"id":344,"name":"Probability Theory","url":"https://www.academia.edu/Documents/in/Probability_Theory?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="815" href="https://www.academia.edu/Documents/in/Epistemology">Epistemology</a>, <script data-card-contents-for-ri="815" type="text/json">{"id":815,"name":"Epistemology","url":"https://www.academia.edu/Documents/in/Epistemology?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="821" href="https://www.academia.edu/Documents/in/Philosophy_of_Science">Philosophy of Science</a>, <script data-card-contents-for-ri="821" type="text/json">{"id":821,"name":"Philosophy of Science","url":"https://www.academia.edu/Documents/in/Philosophy_of_Science?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" 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2014.","downloadable_attachments":[{"id":41229519,"asset_id":20296195,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":35251999,"first_name":"Susan","last_name":"Haack","domain_name":"miami","page_name":"SusanHaack","display_name":"Susan Haack","profile_url":"https://miami.academia.edu/SusanHaack?f_ri=22613","photo":"https://0.academia-photos.com/35251999/10725002/11972416/s65_susan.haack.jpg"}],"research_interests":[{"id":344,"name":"Probability Theory","url":"https://www.academia.edu/Documents/in/Probability_Theory?f_ri=22613","nofollow":false},{"id":815,"name":"Epistemology","url":"https://www.academia.edu/Documents/in/Epistemology?f_ri=22613","nofollow":false},{"id":821,"name":"Philosophy of 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Truth","url":"https://www.academia.edu/Documents/in/Theories_Of_Truth?f_ri=22613"},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613"},{"id":29876,"name":"Theories of truth (Philosophy)","url":"https://www.academia.edu/Documents/in/Theories_of_truth_Philosophy_?f_ri=22613"},{"id":98095,"name":"Epistemology of Testimony","url":"https://www.academia.edu/Documents/in/Epistemology_of_Testimony?f_ri=22613"},{"id":620707,"name":"Philosophy of Law","url":"https://www.academia.edu/Documents/in/Philosophy_of_Law-1?f_ri=22613"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_73155253" data-work_id="73155253" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/73155253/Ruin_Probability_in_a_Generalized_Risk_Process_under_Interest_Force_with_Homogenous_Markov_Chain_Premiums">Ruin Probability in a Generalized Risk Process under Interest Force with Homogenous Markov Chain Premiums</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/73155253" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="55457051798bf5cf8e0691f24170e6cf" rel="nofollow" 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$(".js-view-count-work_73155253").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="73155253"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a>, <script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="864976" href="https://www.academia.edu/Documents/in/Integral_Equation">Integral Equation</a>, <script data-card-contents-for-ri="864976" type="text/json">{"id":864976,"name":"Integral Equation","url":"https://www.academia.edu/Documents/in/Integral_Equation?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="1865832" href="https://www.academia.edu/Documents/in/Ruin_Probability">Ruin Probability</a><script data-card-contents-for-ri="1865832" type="text/json">{"id":1865832,"name":"Ruin Probability","url":"https://www.academia.edu/Documents/in/Ruin_Probability?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=73155253]'), work: {"id":73155253,"title":"Ruin Probability in a Generalized Risk Process under Interest Force with Homogenous Markov Chain Premiums","created_at":"2022-03-05T20:50:15.894-08:00","url":"https://www.academia.edu/73155253/Ruin_Probability_in_a_Generalized_Risk_Process_under_Interest_Force_with_Homogenous_Markov_Chain_Premiums?f_ri=22613","dom_id":"work_73155253","summary":null,"downloadable_attachments":[{"id":81785484,"asset_id":73155253,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":23955566,"first_name":"Duy","last_name":"Quang","domain_name":"independent","page_name":"DuyQuang2","display_name":"Duy Quang","profile_url":"https://independent.academia.edu/DuyQuang2?f_ri=22613","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false},{"id":864976,"name":"Integral Equation","url":"https://www.academia.edu/Documents/in/Integral_Equation?f_ri=22613","nofollow":false},{"id":1865832,"name":"Ruin Probability","url":"https://www.academia.edu/Documents/in/Ruin_Probability?f_ri=22613","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_39944934" data-work_id="39944934" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/39944934/Probability_Density_Functions_of_Imaginary_and_Complex_Random_Variables">Probability Density Functions of Imaginary and Complex Random Variables</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this report, the probability density functions of imaginary and complex random variables are determined in terms of the probability density function of real random variables. Equivalent expressions of the change of variable theorem are... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_39944934" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this report, the probability density functions of imaginary and complex random variables are determined in terms of the probability density function of real random variables. Equivalent expressions of the change of variable theorem are obtained in both cases. While real probability density functions have always real positive values, the probability density functions of imaginary random variables are found to be always imaginary negative. Furthermore, complex random functions of a single variable can be described by complex probability density functions with positive real parts and negative imaginary parts (using a proposed modified absolute value operator for complex numbers). The probability density functions of complex random variables with independent random components are differential values which tend to zero, and therefore, they must be described using probability density density functions. Standard transformations of imaginary and complex random variables can be defined similarly to real standard transformations but using Hadamard (element-wise) operations. The standard normal imaginary and standard normal complex random variables are presented as examples. The behavior of the square root of zero-mean random variables is also presented and analyzed.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/39944934" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="be0dbb08bbac4d9e1485271dc3452677" rel="nofollow" data-download="{"attachment_id":60132158,"asset_id":39944934,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/60132158/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="57882510" href="https://independent.academia.edu/HugoHernandez97">Hugo Hernandez</a><script data-card-contents-for-user="57882510" type="text/json">{"id":57882510,"first_name":"Hugo","last_name":"Hernandez","domain_name":"independent","page_name":"HugoHernandez97","display_name":"Hugo Hernandez","profile_url":"https://independent.academia.edu/HugoHernandez97?f_ri=22613","photo":"https://0.academia-photos.com/57882510/15170791/15861515/s65_hugo.hernandez.png"}</script></span></span></li><li class="js-paper-rank-work_39944934 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="39944934"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 39944934, container: ".js-paper-rank-work_39944934", }); 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$(".js-view-count[data-work-id=39944934]").text(description); $(".js-view-count-work_39944934").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_39944934").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="39944934"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="892" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a>, <script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="31412" href="https://www.academia.edu/Documents/in/Probability_and_Mathematical_Statistics">Probability and Mathematical Statistics</a><script data-card-contents-for-ri="31412" type="text/json">{"id":31412,"name":"Probability and Mathematical Statistics","url":"https://www.academia.edu/Documents/in/Probability_and_Mathematical_Statistics?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=39944934]'), work: {"id":39944934,"title":"Probability Density Functions of Imaginary and Complex Random Variables","created_at":"2019-07-27T09:08:05.209-07:00","url":"https://www.academia.edu/39944934/Probability_Density_Functions_of_Imaginary_and_Complex_Random_Variables?f_ri=22613","dom_id":"work_39944934","summary":"In this report, the probability density functions of imaginary and complex random variables are determined in terms of the probability density function of real random variables. Equivalent expressions of the change of variable theorem are obtained in both cases. While real probability density functions have always real positive values, the probability density functions of imaginary random variables are found to be always imaginary negative. Furthermore, complex random functions of a single variable can be described by complex probability density functions with positive real parts and negative imaginary parts (using a proposed modified absolute value operator for complex numbers). The probability density functions of complex random variables with independent random components are differential values which tend to zero, and therefore, they must be described using probability density density functions. Standard transformations of imaginary and complex random variables can be defined similarly to real standard transformations but using Hadamard (element-wise) operations. The standard normal imaginary and standard normal complex random variables are presented as examples. The behavior of the square root of zero-mean random variables is also presented and analyzed.","downloadable_attachments":[{"id":60132158,"asset_id":39944934,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":57882510,"first_name":"Hugo","last_name":"Hernandez","domain_name":"independent","page_name":"HugoHernandez97","display_name":"Hugo Hernandez","profile_url":"https://independent.academia.edu/HugoHernandez97?f_ri=22613","photo":"https://0.academia-photos.com/57882510/15170791/15861515/s65_hugo.hernandez.png"}],"research_interests":[{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=22613","nofollow":false},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false},{"id":31412,"name":"Probability and Mathematical Statistics","url":"https://www.academia.edu/Documents/in/Probability_and_Mathematical_Statistics?f_ri=22613","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_34866203" data-work_id="34866203" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/34866203/Certificate_Introduction_to_Probability_and_Data_by_Duke_University_on_Coursera">Certificate-Introduction to Probability and Data by Duke University on Coursera</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/34866203" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="fc5e44f84d88d9109a17ec2426524963" rel="nofollow" data-download="{"attachment_id":54725184,"asset_id":34866203,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/54725184/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="68496386" href="https://sust.academia.edu/MohammadShahidulIslam">Mohammad Shahidul Islam</a><script data-card-contents-for-user="68496386" type="text/json">{"id":68496386,"first_name":"Mohammad Shahidul","last_name":"Islam","domain_name":"sust","page_name":"MohammadShahidulIslam","display_name":"Mohammad Shahidul Islam","profile_url":"https://sust.academia.edu/MohammadShahidulIslam?f_ri=22613","photo":"https://0.academia-photos.com/68496386/17780474/19274296/s65_mohammad_shahidul.islam.jpg"}</script></span></span></li><li class="js-paper-rank-work_34866203 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="34866203"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 34866203, container: ".js-paper-rank-work_34866203", }); 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$(".js-view-count[data-work-id=34866203]").text(description); $(".js-view-count-work_34866203").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_34866203").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="34866203"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="892" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a><script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=34866203]'), work: {"id":34866203,"title":"Certificate-Introduction to Probability and Data by Duke University on Coursera","created_at":"2017-10-15T15:13:59.023-07:00","url":"https://www.academia.edu/34866203/Certificate_Introduction_to_Probability_and_Data_by_Duke_University_on_Coursera?f_ri=22613","dom_id":"work_34866203","summary":null,"downloadable_attachments":[{"id":54725184,"asset_id":34866203,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":68496386,"first_name":"Mohammad Shahidul","last_name":"Islam","domain_name":"sust","page_name":"MohammadShahidulIslam","display_name":"Mohammad Shahidul Islam","profile_url":"https://sust.academia.edu/MohammadShahidulIslam?f_ri=22613","photo":"https://0.academia-photos.com/68496386/17780474/19274296/s65_mohammad_shahidul.islam.jpg"}],"research_interests":[{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=22613","nofollow":false},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_45648515" data-work_id="45648515" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/45648515/A_STUDY_ON_THEORETICAL_FOUNDATIONS_FOR_DATA_SCIENCE_IN_THE_FIELD_OF_STATISTICS_AND_PROBABILITY">A STUDY ON THEORETICAL FOUNDATIONS FOR DATA SCIENCE IN THE FIELD OF STATISTICS AND PROBABILITY</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Data Science as a scientific discipline is influenced by informatics, computer science, mathematics, operations research, and statistics as well as the applied sciences. Many research papers have come across that probability and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_45648515" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Data Science as a scientific discipline is influenced by informatics, computer science, mathematics, operations research, and statistics as well as the applied sciences. Many research papers have come across that probability and statistics are the crucial prerequisites for data science. Having a good understanding of these two aspects would not only support with the concepts but will also help to attain of becoming a data science professional. This paper discusses the basics of probability and statistics in the context of data science with some advanced concepts</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/45648515" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="76d73177ba245224da3bf7b33969bcfc" rel="nofollow" data-download="{"attachment_id":66146224,"asset_id":45648515,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/66146224/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="39122404" href="https://iaeme.academia.edu/publication">IAEME Publication</a><script data-card-contents-for-user="39122404" type="text/json">{"id":39122404,"first_name":"IAEME","last_name":"Publication","domain_name":"iaeme","page_name":"publication","display_name":"IAEME Publication","profile_url":"https://iaeme.academia.edu/publication?f_ri=22613","photo":"https://0.academia-photos.com/39122404/12178523/13563629/s65_iaeme.publication.jpg"}</script></span></span></li><li class="js-paper-rank-work_45648515 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="45648515"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 45648515, container: ".js-paper-rank-work_45648515", }); 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Many research papers have come across that probability and statistics are the crucial prerequisites for data science. Having a good understanding of these two aspects would not only support with the concepts but will also help to attain of becoming a data science professional. This paper discusses the basics of probability and statistics in the context of data science with some advanced concepts","downloadable_attachments":[{"id":66146224,"asset_id":45648515,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":39122404,"first_name":"IAEME","last_name":"Publication","domain_name":"iaeme","page_name":"publication","display_name":"IAEME Publication","profile_url":"https://iaeme.academia.edu/publication?f_ri=22613","photo":"https://0.academia-photos.com/39122404/12178523/13563629/s65_iaeme.publication.jpg"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=22613","nofollow":false},{"id":329,"name":"Algebra","url":"https://www.academia.edu/Documents/in/Algebra?f_ri=22613","nofollow":false},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=22613","nofollow":false},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false},{"id":69100,"name":"Data Science","url":"https://www.academia.edu/Documents/in/Data_Science?f_ri=22613"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_40861250 coauthored" data-work_id="40861250" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/40861250/Exponential_Gamma_Distribution">Exponential-Gamma Distribution</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Statistical distributions are widely applied to describe different real world phenomena. As a result of the usefulness of statistical distributions, many researchers have studied their theory extensively and new distributions are... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_40861250" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Statistical distributions are widely applied to describe different real world phenomena. As a result of the usefulness of statistical distributions, many researchers have studied their theory extensively and new distributions are developed. The quest for developing more efficient and flexible probability distribution still remain strong in the field of probability theory and statistics. In this paper, we propose a new probability distribution called Exponential-Gamma distribution and derive appropriate expressions for its statistical properties.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/40861250" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="77ef0801b6fd60ab4d38306af43a9d53" rel="nofollow" data-download="{"attachment_id":61146822,"asset_id":40861250,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/61146822/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="31000870" href="https://unadportal.academia.edu/ayenitaiwo">Taiwo M Ayeni</a><script data-card-contents-for-user="31000870" type="text/json">{"id":31000870,"first_name":"Taiwo","last_name":"Ayeni","domain_name":"unadportal","page_name":"ayenitaiwo","display_name":"Taiwo M Ayeni","profile_url":"https://unadportal.academia.edu/ayenitaiwo?f_ri=22613","photo":"https://0.academia-photos.com/31000870/35332839/34705454/s65_taiwo.ayeni.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-40861250">+2</span><div class="hidden js-additional-users-40861250"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/OgunwaleOD">Ogunwale O. D</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/AdewusiOluwasesanAdeoye">Adewusi Oluwasesan Adeoye</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-40861250'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-40861250').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_40861250 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="40861250"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 40861250, container: ".js-paper-rank-work_40861250", }); });</script></li><li class="js-percentile-work_40861250 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40861250; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_40861250"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_40861250 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="40861250"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40861250; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40861250]").text(description); $(".js-view-count-work_40861250").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_40861250").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="40861250"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i></div><span class="InlineList-item-text u-textTruncate u-pl6x"><a class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a><script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (false) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=40861250]'), work: {"id":40861250,"title":"Exponential-Gamma Distribution","created_at":"2019-11-06T12:17:55.583-08:00","url":"https://www.academia.edu/40861250/Exponential_Gamma_Distribution?f_ri=22613","dom_id":"work_40861250","summary":"Statistical distributions are widely applied to describe different real world phenomena. As a result of the usefulness of statistical distributions, many researchers have studied their theory extensively and new distributions are developed. The quest for developing more efficient and flexible probability distribution still remain strong in the field of probability theory and statistics. In this paper, we propose a new probability distribution called Exponential-Gamma distribution and derive appropriate expressions for its statistical properties.","downloadable_attachments":[{"id":61146822,"asset_id":40861250,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":31000870,"first_name":"Taiwo","last_name":"Ayeni","domain_name":"unadportal","page_name":"ayenitaiwo","display_name":"Taiwo M Ayeni","profile_url":"https://unadportal.academia.edu/ayenitaiwo?f_ri=22613","photo":"https://0.academia-photos.com/31000870/35332839/34705454/s65_taiwo.ayeni.jpg"},{"id":135649369,"first_name":"Ogunwale O.","last_name":"D","domain_name":"independent","page_name":"OgunwaleOD","display_name":"Ogunwale O. D","profile_url":"https://independent.academia.edu/OgunwaleOD?f_ri=22613","photo":"/images/s65_no_pic.png"},{"id":139095329,"first_name":"Adewusi Oluwasesan","last_name":"Adeoye","domain_name":"independent","page_name":"AdewusiOluwasesanAdeoye","display_name":"Adewusi Oluwasesan Adeoye","profile_url":"https://independent.academia.edu/AdewusiOluwasesanAdeoye?f_ri=22613","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_26890982" data-work_id="26890982" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/26890982/Statistik_I_Deskriptive_und_explorative_Statistik">Statistik I - Deskriptive und explorative Statistik</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">Foliensatz für Statistik I in den berufsbegleitenden Studiengängen BWL und Wirtschaftsingenieurwesen in den Fachbereichen Automatisierung und Informatik sowie Wirtschaftswissenschaften im Sommersemester 2016.</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/26890982" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a 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1) do curso de Probabilidade e Processos Estocásticos, Pós-graduação em Engenharia Elétrica</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/9386460" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b33d495e5f7b02e4225618b213100bc5" rel="nofollow" data-download="{"attachment_id":35636421,"asset_id":9386460,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/35636421/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="166797" href="https://ufpe.academia.edu/hmdeoliveira">h.m. de oliveira</a><script data-card-contents-for-user="166797" type="text/json">{"id":166797,"first_name":"h.m.","last_name":"de oliveira","domain_name":"ufpe","page_name":"hmdeoliveira","display_name":"h.m. de 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$(".js-percentile-work_9386460"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_9386460 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="9386460"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 9386460; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=9386460]").text(description); $(".js-view-count-work_9386460").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_9386460").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="9386460"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="347" href="https://www.academia.edu/Documents/in/Stochastic_Process">Stochastic Process</a>, <script data-card-contents-for-ri="347" type="text/json">{"id":347,"name":"Stochastic Process","url":"https://www.academia.edu/Documents/in/Stochastic_Process?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a><script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=9386460]'), work: {"id":9386460,"title":"Slides parciais do curso PROBABILIDADE E PROCESSOS ESTOCASTICOS","created_at":"2014-11-18T21:59:45.185-08:00","url":"https://www.academia.edu/9386460/Slides_parciais_do_curso_PROBABILIDADE_E_PROCESSOS_ESTOCASTICOS?f_ri=22613","dom_id":"work_9386460","summary":"Aulas (parte 1) do curso de Probabilidade e Processos Estocásticos, Pós-graduação em Engenharia Elétrica","downloadable_attachments":[{"id":35636421,"asset_id":9386460,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":166797,"first_name":"h.m.","last_name":"de oliveira","domain_name":"ufpe","page_name":"hmdeoliveira","display_name":"h.m. de oliveira","profile_url":"https://ufpe.academia.edu/hmdeoliveira?f_ri=22613","photo":"https://0.academia-photos.com/166797/42212/29039976/s65_h.m..de_oliveira.jpeg"}],"research_interests":[{"id":347,"name":"Stochastic Process","url":"https://www.academia.edu/Documents/in/Stochastic_Process?f_ri=22613","nofollow":false},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_16450809" data-work_id="16450809" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/16450809/THE_GEOMETRY_OF_UNCERTAINTY">THE GEOMETRY OF UNCERTAINTY</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">WHY A MATHEMATICS OF UNCERTAINTY? - probabilities do not represent well ignorance and lack of data; - evidence is normally limited, rather than infinite as assumed by (frequentist) probability; - expert knowledge needs often to be... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_16450809" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">WHY A MATHEMATICS OF UNCERTAINTY?<br /><br />- probabilities do not represent well ignorance and lack of data;<br />- evidence is normally limited, rather than infinite as assumed by (frequentist) probability;<br />- expert knowledge needs often to be combined with hard evidence;<br />- in extreme cases (rare events or far-future predictions) very little data;<br />- bottom line: not enough evidence to determine the actual probability describing the problem.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/16450809" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0e22eed4ccb3952bd0ff6068c934ae44" rel="nofollow" data-download="{"attachment_id":38999332,"asset_id":16450809,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/38999332/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa 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class="js-view-count-work_16450809 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="16450809"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16450809; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16450809]").text(description); $(".js-view-count-work_16450809").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_16450809").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="16450809"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">21</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="344" href="https://www.academia.edu/Documents/in/Probability_Theory">Probability Theory</a>, <script data-card-contents-for-ri="344" type="text/json">{"id":344,"name":"Probability Theory","url":"https://www.academia.edu/Documents/in/Probability_Theory?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="358" href="https://www.academia.edu/Documents/in/Convex_Geometry">Convex Geometry</a>, <script data-card-contents-for-ri="358" type="text/json">{"id":358,"name":"Convex Geometry","url":"https://www.academia.edu/Documents/in/Convex_Geometry?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="892" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4060" href="https://www.academia.edu/Documents/in/Applied_Statistics">Applied Statistics</a><script data-card-contents-for-ri="4060" type="text/json">{"id":4060,"name":"Applied Statistics","url":"https://www.academia.edu/Documents/in/Applied_Statistics?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=16450809]'), work: {"id":16450809,"title":"THE GEOMETRY OF UNCERTAINTY","created_at":"2015-10-04T09:26:07.364-07:00","url":"https://www.academia.edu/16450809/THE_GEOMETRY_OF_UNCERTAINTY?f_ri=22613","dom_id":"work_16450809","summary":"WHY A MATHEMATICS OF UNCERTAINTY?\n\n- probabilities do not represent well ignorance and lack of data;\n- evidence is normally limited, rather than infinite as assumed by (frequentist) probability;\n- expert knowledge needs often to be combined with hard evidence;\n- in extreme cases (rare events or far-future predictions) very little data;\n- bottom line: not enough evidence to determine the actual probability describing the problem.","downloadable_attachments":[{"id":38999332,"asset_id":16450809,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":366407,"first_name":"Fabio","last_name":"Cuzzolin","domain_name":"oxfordbrookes","page_name":"FabioCuzzolin","display_name":"Fabio Cuzzolin","profile_url":"https://oxfordbrookes.academia.edu/FabioCuzzolin?f_ri=22613","photo":"https://0.academia-photos.com/366407/112374/61740579/s65_fabio.cuzzolin.jpg"}],"research_interests":[{"id":344,"name":"Probability Theory","url":"https://www.academia.edu/Documents/in/Probability_Theory?f_ri=22613","nofollow":false},{"id":358,"name":"Convex Geometry","url":"https://www.academia.edu/Documents/in/Convex_Geometry?f_ri=22613","nofollow":false},{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=22613","nofollow":false},{"id":4060,"name":"Applied Statistics","url":"https://www.academia.edu/Documents/in/Applied_Statistics?f_ri=22613","nofollow":false},{"id":5436,"name":"Combinatorics","url":"https://www.academia.edu/Documents/in/Combinatorics?f_ri=22613"},{"id":6404,"name":"Reasoning about Uncertainty","url":"https://www.academia.edu/Documents/in/Reasoning_about_Uncertainty?f_ri=22613"},{"id":13000,"name":"Dempster-Shafer Analysis","url":"https://www.academia.edu/Documents/in/Dempster-Shafer_Analysis?f_ri=22613"},{"id":16097,"name":"Decision Making Under Uncertainty","url":"https://www.academia.edu/Documents/in/Decision_Making_Under_Uncertainty?f_ri=22613"},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613"},{"id":31412,"name":"Probability and Mathematical Statistics","url":"https://www.academia.edu/Documents/in/Probability_and_Mathematical_Statistics?f_ri=22613"},{"id":33069,"name":"Probability","url":"https://www.academia.edu/Documents/in/Probability?f_ri=22613"},{"id":54418,"name":"Geometry","url":"https://www.academia.edu/Documents/in/Geometry?f_ri=22613"},{"id":61603,"name":"Uncertainty","url":"https://www.academia.edu/Documents/in/Uncertainty?f_ri=22613"},{"id":94223,"name":"Imprecise Probability","url":"https://www.academia.edu/Documents/in/Imprecise_Probability?f_ri=22613"},{"id":94225,"name":"Belief Functions","url":"https://www.academia.edu/Documents/in/Belief_Functions?f_ri=22613"},{"id":122987,"name":"Non-probabilistic Modeling and Imprecise Probabilities","url":"https://www.academia.edu/Documents/in/Non-probabilistic_Modeling_and_Imprecise_Probabilities?f_ri=22613"},{"id":290552,"name":"Uncertainty analysis","url":"https://www.academia.edu/Documents/in/Uncertainty_analysis?f_ri=22613"},{"id":304534,"name":"Dempster-Shafer Theory of Evidence","url":"https://www.academia.edu/Documents/in/Dempster-Shafer_Theory_of_Evidence?f_ri=22613"},{"id":571797,"name":"Introduction to Probability","url":"https://www.academia.edu/Documents/in/Introduction_to_Probability?f_ri=22613"},{"id":655694,"name":"Subjective Probability - Fuzzy Theory and Belief Functions","url":"https://www.academia.edu/Documents/in/Subjective_Probability_-_Fuzzy_Theory_and_Belief_Functions?f_ri=22613"},{"id":1005286,"name":"Dempster Shafer Theory","url":"https://www.academia.edu/Documents/in/Dempster_Shafer_Theory?f_ri=22613"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_71081510" data-work_id="71081510" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/71081510/Time_Series_Analysis_and_Forecasting_of_Caesarian_Section_Births_in_Ghana">Time Series Analysis and Forecasting of Caesarian Section Births in Ghana</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Caesarian Section (CS) rates have been known to have geographical varaitions. The purpose of this paper was to determine Ghana&#39;s situation (regional trend) and also to provide a two-year forcast estimates for the ten (10) regions of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_71081510" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Caesarian Section (CS) rates have been known to have geographical varaitions. The purpose of this paper was to determine Ghana&#39;s situation (regional trend) and also to provide a two-year forcast estimates for the ten (10) regions of Ghana. The data was longitudinal and comprised monthly CS records of women from 2008 to 2017. The dataset was divided into training and testing dataset. A total of eighty four (84) months were used as the training dataset and the remaining thirty six (36) months were used as testing dataset. The ARIMA methodology was applied in the analysis. Augmented Dicker-Fuller (ADF), KPSS and the Philips-Perron (PP) unit root tests were employed to test for stationarity of the series plot. KPSS (which is known to give more robust results) and PP test consistently showed that the series was stationary (p ＜ 0.05) for all ten (10) regions, although there were some conflicting results with the ADF test for some regions. Tentative models were formulated for each regi...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/71081510" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0e1d577ab93c1025352611d870aa4cba" rel="nofollow" data-download="{"attachment_id":80581868,"asset_id":71081510,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/80581868/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34873658" href="https://independent.academia.edu/JOTOO">JOSEPH OTOO</a><script data-card-contents-for-user="34873658" type="text/json">{"id":34873658,"first_name":"JOSEPH","last_name":"OTOO","domain_name":"independent","page_name":"JOTOO","display_name":"JOSEPH OTOO","profile_url":"https://independent.academia.edu/JOTOO?f_ri=22613","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_71081510 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="71081510"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 71081510, container: ".js-paper-rank-work_71081510", }); 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$(".js-view-count[data-work-id=71081510]").text(description); $(".js-view-count-work_71081510").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_71081510").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="71081510"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="300" href="https://www.academia.edu/Documents/in/Mathematics">Mathematics</a>, <script data-card-contents-for-ri="300" type="text/json">{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="305" href="https://www.academia.edu/Documents/in/Applied_Mathematics">Applied Mathematics</a>, <script data-card-contents-for-ri="305" type="text/json">{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="1352" href="https://www.academia.edu/Documents/in/Multivariate_Statistics">Multivariate Statistics</a>, <script data-card-contents-for-ri="1352" type="text/json">{"id":1352,"name":"Multivariate Statistics","url":"https://www.academia.edu/Documents/in/Multivariate_Statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4060" href="https://www.academia.edu/Documents/in/Applied_Statistics">Applied Statistics</a><script data-card-contents-for-ri="4060" type="text/json">{"id":4060,"name":"Applied Statistics","url":"https://www.academia.edu/Documents/in/Applied_Statistics?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=71081510]'), work: {"id":71081510,"title":"Time Series Analysis and Forecasting of Caesarian Section Births in Ghana","created_at":"2022-02-11T00:09:15.544-08:00","url":"https://www.academia.edu/71081510/Time_Series_Analysis_and_Forecasting_of_Caesarian_Section_Births_in_Ghana?f_ri=22613","dom_id":"work_71081510","summary":"Caesarian Section (CS) rates have been known to have geographical varaitions. The purpose of this paper was to determine Ghana\u0026#39;s situation (regional trend) and also to provide a two-year forcast estimates for the ten (10) regions of Ghana. The data was longitudinal and comprised monthly CS records of women from 2008 to 2017. The dataset was divided into training and testing dataset. A total of eighty four (84) months were used as the training dataset and the remaining thirty six (36) months were used as testing dataset. The ARIMA methodology was applied in the analysis. Augmented Dicker-Fuller (ADF), KPSS and the Philips-Perron (PP) unit root tests were employed to test for stationarity of the series plot. KPSS (which is known to give more robust results) and PP test consistently showed that the series was stationary (p ＜ 0.05) for all ten (10) regions, although there were some conflicting results with the ADF test for some regions. Tentative models were formulated for each regi...","downloadable_attachments":[{"id":80581868,"asset_id":71081510,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":34873658,"first_name":"JOSEPH","last_name":"OTOO","domain_name":"independent","page_name":"JOTOO","display_name":"JOSEPH OTOO","profile_url":"https://independent.academia.edu/JOTOO?f_ri=22613","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=22613","nofollow":false},{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=22613","nofollow":false},{"id":1352,"name":"Multivariate Statistics","url":"https://www.academia.edu/Documents/in/Multivariate_Statistics?f_ri=22613","nofollow":false},{"id":4060,"name":"Applied Statistics","url":"https://www.academia.edu/Documents/in/Applied_Statistics?f_ri=22613","nofollow":false},{"id":10284,"name":"Competing Risk Models","url":"https://www.academia.edu/Documents/in/Competing_Risk_Models?f_ri=22613"},{"id":10610,"name":"Survival Analysis","url":"https://www.academia.edu/Documents/in/Survival_Analysis?f_ri=22613"},{"id":14585,"name":"Statistical Modeling","url":"https://www.academia.edu/Documents/in/Statistical_Modeling?f_ri=22613"},{"id":16682,"name":"Mathematical Modelling","url":"https://www.academia.edu/Documents/in/Mathematical_Modelling?f_ri=22613"},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613"},{"id":30485,"name":"Time series analysis","url":"https://www.academia.edu/Documents/in/Time_series_analysis?f_ri=22613"},{"id":566534,"name":"Epidemiology and biostatistics","url":"https://www.academia.edu/Documents/in/Epidemiology_and_biostatistics?f_ri=22613"},{"id":1003619,"name":"Time Series Analysis and Forecasting","url":"https://www.academia.edu/Documents/in/Time_Series_Analysis_and_Forecasting?f_ri=22613"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_9696198" data-work_id="9696198" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/9696198/GROUND_TRUTH_PLANNING_FOR_SYNTHETIC_APERTURE_RADAR_SAR_ADDRESSING_VARIOUS_CHALLENGES_USING_STATISTICAL_APPROACH">GROUND TRUTH PLANNING FOR SYNTHETIC APERTURE RADAR (SAR): ADDRESSING VARIOUS CHALLENGES USING STATISTICAL APPROACH</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">While planning target parameter retrieval experiment in farmers' fields using Synthetic Aperture Radar (SAR) remote sensing, it is very important to answer three questions. The first question is, "How large should a farmer's field be from... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_9696198" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">While planning target parameter retrieval experiment in farmers' fields using Synthetic Aperture Radar (SAR) remote sensing, it is very important to answer three questions. The first question is, "How large should a farmer's field be from where the target parameter sample should be collected ?‟. The answer to this question is very critical particularly when the experiment is to be carried out in South Asian countries where <br />in general, agricultural land holding by an individual farmer is small. The second question of importance is that “what should be the sample size for model development?” or in other words <br />“how many such large enough farmers‟ fields needs to be taken <br />so that enough target parameter samples will be available to be able to develop a meaningful relationship between remotely sensed parameter and the observed target parameter ? ”. The third important question is what should be the minimum sample size for meaningful model validation? In this paper answers to all these questions are obtained by adopting statistical approach when the experiment is planned for target parameter retrieval using synthetic Aperture radar (SAR). The required size of the sampling unit is determined by taking into account the characteristic-fading phenomenon of SAR signal and probabilistic statistical approach based upon the margin of error permissible by the experimenter in estimating average. The answer to the second question about how many farmers fields or in other words, sampling units be chosen during ground truth data collection such that a nearly true relationship can be developed between observed target parameter on ground and SAR backscatter, is obtained based upon the margin of error permissible by the experimenter in a regression estimate. Finally answer to third question has been reached by developing a procedure using "precision power approach‟ to arrive at the minimum size of validation sample required by the experimenter for a meaningful validation exercise. It should be noted that the approach presented in this paper to determine sample size for model development and model validation are general in nature and are equally applicable to other remote sensing data sets as well.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/9696198" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="9a943875c4ad80cb2bcf18de03509cf4" rel="nofollow" data-download="{"attachment_id":35887361,"asset_id":9696198,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/35887361/download_file?st=MTczMjczNDU2Myw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="20142454" href="https://iirs.academia.edu/DrHariShankerSrivastava">Dr. Hari Shanker Srivastava</a><script data-card-contents-for-user="20142454" type="text/json">{"id":20142454,"first_name":"Dr. Hari Shanker","last_name":"Srivastava","domain_name":"iirs","page_name":"DrHariShankerSrivastava","display_name":"Dr. Hari Shanker Srivastava","profile_url":"https://iirs.academia.edu/DrHariShankerSrivastava?f_ri=22613","photo":"https://0.academia-photos.com/20142454/6863790/17042229/s65_dr._hari_shanker.srivastava.jpg"}</script></span></span></li><li class="js-paper-rank-work_9696198 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="9696198"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 9696198, container: ".js-paper-rank-work_9696198", }); 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$(".js-view-count[data-work-id=9696198]").text(description); $(".js-view-count-work_9696198").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_9696198").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="9696198"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="22613" href="https://www.academia.edu/Documents/in/Probability_and_statistics">Probability and statistics</a>, <script data-card-contents-for-ri="22613" type="text/json">{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="61120" href="https://www.academia.edu/Documents/in/Model_validation">Model validation</a>, <script data-card-contents-for-ri="61120" type="text/json">{"id":61120,"name":"Model validation","url":"https://www.academia.edu/Documents/in/Model_validation?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="139202" href="https://www.academia.edu/Documents/in/Synthetic_Aperture_Radar">Synthetic Aperture Radar</a>, <script data-card-contents-for-ri="139202" type="text/json">{"id":139202,"name":"Synthetic Aperture Radar","url":"https://www.academia.edu/Documents/in/Synthetic_Aperture_Radar?f_ri=22613","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="822358" href="https://www.academia.edu/Documents/in/Ground_Truth">Ground Truth</a><script data-card-contents-for-ri="822358" type="text/json">{"id":822358,"name":"Ground Truth","url":"https://www.academia.edu/Documents/in/Ground_Truth?f_ri=22613","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=9696198]'), work: {"id":9696198,"title":"GROUND TRUTH PLANNING FOR SYNTHETIC APERTURE RADAR (SAR): ADDRESSING VARIOUS CHALLENGES USING STATISTICAL APPROACH","created_at":"2014-12-09T12:00:31.645-08:00","url":"https://www.academia.edu/9696198/GROUND_TRUTH_PLANNING_FOR_SYNTHETIC_APERTURE_RADAR_SAR_ADDRESSING_VARIOUS_CHALLENGES_USING_STATISTICAL_APPROACH?f_ri=22613","dom_id":"work_9696198","summary":"While planning target parameter retrieval experiment in farmers' fields using Synthetic Aperture Radar (SAR) remote sensing, it is very important to answer three questions. The first question is, \"How large should a farmer's field be from where the target parameter sample should be collected ?‟. The answer to this question is very critical particularly when the experiment is to be carried out in South Asian countries where\r\nin general, agricultural land holding by an individual farmer is small. The second question of importance is that “what should be the sample size for model development?” or in other words\r\n“how many such large enough farmers‟ fields needs to be taken\r\nso that enough target parameter samples will be available to be able to develop a meaningful relationship between remotely sensed parameter and the observed target parameter ? ”. The third important question is what should be the minimum sample size for meaningful model validation? In this paper answers to all these questions are obtained by adopting statistical approach when the experiment is planned for target parameter retrieval using synthetic Aperture radar (SAR). The required size of the sampling unit is determined by taking into account the characteristic-fading phenomenon of SAR signal and probabilistic statistical approach based upon the margin of error permissible by the experimenter in estimating average. The answer to the second question about how many farmers fields or in other words, sampling units be chosen during ground truth data collection such that a nearly true relationship can be developed between observed target parameter on ground and SAR backscatter, is obtained based upon the margin of error permissible by the experimenter in a regression estimate. Finally answer to third question has been reached by developing a procedure using \"precision power approach‟ to arrive at the minimum size of validation sample required by the experimenter for a meaningful validation exercise. It should be noted that the approach presented in this paper to determine sample size for model development and model validation are general in nature and are equally applicable to other remote sensing data sets as well.","downloadable_attachments":[{"id":35887361,"asset_id":9696198,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":20142454,"first_name":"Dr. Hari Shanker","last_name":"Srivastava","domain_name":"iirs","page_name":"DrHariShankerSrivastava","display_name":"Dr. Hari Shanker Srivastava","profile_url":"https://iirs.academia.edu/DrHariShankerSrivastava?f_ri=22613","photo":"https://0.academia-photos.com/20142454/6863790/17042229/s65_dr._hari_shanker.srivastava.jpg"}],"research_interests":[{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=22613","nofollow":false},{"id":61120,"name":"Model validation","url":"https://www.academia.edu/Documents/in/Model_validation?f_ri=22613","nofollow":false},{"id":139202,"name":"Synthetic Aperture Radar","url":"https://www.academia.edu/Documents/in/Synthetic_Aperture_Radar?f_ri=22613","nofollow":false},{"id":822358,"name":"Ground Truth","url":"https://www.academia.edu/Documents/in/Ground_Truth?f_ri=22613","nofollow":false},{"id":883714,"name":"Modelling Development","url":"https://www.academia.edu/Documents/in/Modelling_Development?f_ri=22613"},{"id":1642759,"name":"Radarsat","url":"https://www.academia.edu/Documents/in/Radarsat?f_ri=22613"}]}, }) } })();</script></ul></li></ul></div></div></div><div class="u-taCenter Pagination"><ul class="pagination"><li class="next_page"><a href="/Documents/in/Probability_and_statistics?after=50%2C9696198" rel="next">Next</a></li><li class="last next"><a href="/Documents/in/Probability_and_statistics?page=last">Last »</a></li></ul></div></div><div class="hidden-xs hidden-sm"><div class="u-pl6x"><div 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