<?xml version="1.0" encoding="utf-8" standalone="yes" ?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Logistic on It was simple</title>
    <link>https://hansjoerg.me/tags/logistic/</link>
    <description>Recent content in Logistic on It was simple</description>
    <generator>Hugo -- gohugo.io</generator>
    <copyright>Hansjörg Plieninger</copyright>
    <lastBuildDate>Mon, 13 May 2019 00:00:00 +0000</lastBuildDate>
    
	<atom:link href="https://hansjoerg.me/tags/logistic/index.xml" rel="self" type="application/rss+xml" />
    
    
    <item>
      <title>Regression Modeling With Proportion Data (Part 2)</title>
      <link>https://hansjoerg.me/2019/05/13/regression-modeling-with-proportion-data-part-2/</link>
      <pubDate>Mon, 13 May 2019 00:00:00 +0000</pubDate>
      
      <guid>https://hansjoerg.me/2019/05/13/regression-modeling-with-proportion-data-part-2/</guid>
      <description>Data Analyses: Beta and Quasi-Binomial Regression Results Plot Model Comparison Effect Size    In the first part of this post, I demonstrated how beta and quasi-binomial regression can be used with dependent variables that are proportions or ratios. I applied these models to attendance rates of the German Handball-Bundesliga. In the second part, I want to investigate whether attendance increased after the World Championship that took place in January 2019 in Denmark and Germany (with a new spectator record).</description>
    </item>
    
    <item>
      <title>Regression Modeling With Proportion Data (Part 1)</title>
      <link>https://hansjoerg.me/2019/05/10/regression-modeling-with-proportion-data-part-1/</link>
      <pubDate>Fri, 10 May 2019 00:00:00 +0000</pubDate>
      
      <guid>https://hansjoerg.me/2019/05/10/regression-modeling-with-proportion-data-part-1/</guid>
      <description>Modeling Proportion Data Application: Handball-Bundesliga Setup Selected Variables  Initial Results for Beta Regression Illustrative Plot of Estimates Residuals  Model Comparisons Models Considered Model Performance  Prediction of Future Matches Resources   As a data scientist, one often encounters dependent variables that are proportions: for example, the number of successes divided by the number of attempts, party vote, proportion of money spent for something, or the attendance rate of public events.</description>
    </item>
    
  </channel>
</rss>