<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>statistics on Tom Roth</title><link>https://tomroth.dev/tags/statistics/</link><description>Recent content in statistics on Tom Roth</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Wed, 03 May 2017 00:00:00 +0000</lastBuildDate><atom:link href="https://tomroth.dev/tags/statistics/index.xml" rel="self" type="application/rss+xml"/><item><title>Mean, median, or mode? When to use each of them.</title><link>https://tomroth.dev/median/</link><pubDate>Wed, 03 May 2017 00:00:00 +0000</pubDate><guid>https://tomroth.dev/median/</guid><description>Pretend you wish to get an idea of the average value of your dataset. You&amp;rsquo;ve got a few choices available to you, of which the most common three are the mean, median and mode.
You might argue that the mode is a better choice than the median and the mean, mostly based on the below graphic.
But the mode suffers from other problems. It will fail you on an exponential distribution, or on a log distribution.</description></item><item><title>Linear Regression in R</title><link>https://tomroth.dev/regression/</link><pubDate>Sun, 07 Aug 2016 00:00:00 +0000</pubDate><guid>https://tomroth.dev/regression/</guid><description>As humans, we&amp;rsquo;re drawn to cause and effect. Even when it&amp;rsquo;s wrong of us to do so.
When it gets hot, it&amp;rsquo;s not necessarily because of global warming. An increase in homeless people will not definitively cause an increase in crime rates. Male drivers crash more than female drivers, but maybe this isn&amp;rsquo;t because of their increased testosterone levels.
We&amp;rsquo;ve all heard the correlation vs causation argument before. Our System 2 conscious mind knows that cause and effect isn&amp;rsquo;t always correct.</description></item></channel></rss>