<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Sampling on gdpark.blog</title><link>https://gdpark.blog/tags/sampling/</link><description>Recent content in Sampling on gdpark.blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 18 Nov 2017 00:00:00 +0000</lastBuildDate><atom:link href="https://gdpark.blog/tags/sampling/index.xml" rel="self" type="application/rss+xml"/><item><title>Introduction [Basic Statistics I Studied #1]</title><link>https://gdpark.blog/posts/statistics-01-introduction/</link><pubDate>Sun, 12 Nov 2017 00:00:00 +0000</pubDate><guid>https://gdpark.blog/posts/statistics-01-introduction/</guid><description>A casual dive into why sample variance divides by n-1 and how we use samples to estimate population parameters without measuring absolutely everyone.</description></item><item><title>Derivation of the Student's t-Distribution [Basic Statistics I Studied #6]</title><link>https://gdpark.blog/posts/statistics-06-derivation-of-the-student-s-t-distribution/</link><pubDate>Sat, 18 Nov 2017 00:00:00 +0000</pubDate><guid>https://gdpark.blog/posts/statistics-06-derivation-of-the-student-s-t-distribution/</guid><description>Where the &amp;lsquo;Student&amp;rsquo; name came from, why we ditch the Z-stat when σ is unknown, and a full derivation of the t-distribution PDF — plus properties and a worked example.</description></item></channel></rss>