<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Correlation on Batı Şengül</title>
    <link>http://www.batisengul.co.uk/tags/correlation/</link>
    <description>Recent content in Correlation on Batı Şengül</description>
    <generator>Hugo -- gohugo.io</generator>
    <managingEditor>batisengul@gmail.com</managingEditor>
    <webMaster>batisengul@gmail.com</webMaster>
    <lastBuildDate>Sun, 03 Sep 2017 00:00:00 +0000</lastBuildDate><atom:link href="http://www.batisengul.co.uk/tags/correlation/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Correlation in linear regression</title>
      <link>http://www.batisengul.co.uk/post/correlation-in-linear-regression/</link>
      <pubDate>Sun, 03 Sep 2017 00:00:00 +0000</pubDate>
      <author>batisengul@gmail.com</author>
      <guid>http://www.batisengul.co.uk/post/correlation-in-linear-regression/</guid>
      <description>If you have a data set with large number of predictors, you might use some basic models to try and eliminate some of the predictors that don’t show a significant relationship to the response variable. In such cases it is important to look at the correlation between the predictors. How important? Let’s find out.
Let us consider a very simple example here with two predictors and one response variable.</description>
    </item>
    
  </channel>
</rss>
