<?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 Han's XYZ</title><link>https://han8931.github.io/tags/statistics/</link><description>Recent content in Statistics on Han's XYZ</description><generator>Hugo</generator><language>en</language><managingEditor>tabularasa8931@gmail.com (Han)</managingEditor><webMaster>tabularasa8931@gmail.com (Han)</webMaster><copyright>This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</copyright><lastBuildDate>Tue, 07 Oct 2025 10:42:45 +0900</lastBuildDate><atom:link href="https://han8931.github.io/tags/statistics/index.xml" rel="self" type="application/rss+xml"/><item><title>Statistics</title><link>https://han8931.github.io/studynotes/statistics/</link><pubDate>Sun, 07 Sep 2025 00:00:00 +0000</pubDate><author>tabularasa8931@gmail.com (Han)</author><guid>https://han8931.github.io/studynotes/statistics/</guid><description>&lt;h1 id="-statistics"&gt;📊 Statistics&lt;/h1&gt;
&lt;p&gt;👉 Repository: &lt;a href="https://github.com/Han8931/statistics" target="_blank" rel="noopener noreffer "&gt;Statistics&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;This note collects my study materials on &lt;strong&gt;probability and statistics&lt;/strong&gt;, with a focus on the foundations needed for &lt;strong&gt;data science, machine learning, and deep learning&lt;/strong&gt;. It combines key definitions, derivations, and examples, aiming to make abstract ideas easier to understand and apply.&lt;/p&gt;
&lt;blockquote&gt;
&lt;p&gt;I welcome all comments and suggestions—and I&amp;rsquo;d be happy to improve and grow this note together with you.&lt;/p&gt;
&lt;/blockquote&gt;</description></item></channel></rss>