<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>Statistics - Tag - Zhiverse</title><link>https://yao-chih.netlify.app/en/tags/statistics/</link><description>Statistics - Tag - Zhiverse</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>yaochihh@gmail.com (Josh)</managingEditor><webMaster>yaochihh@gmail.com (Josh)</webMaster><copyright>Zhiverse</copyright><atom:link href="https://yao-chih.netlify.app/en/tags/statistics/" rel="self" type="application/rss+xml"/><item><title>Learning Control Charts</title><link>https://yao-chih.netlify.app/en/learning-control-charts/</link><pubDate>Mon, 06 Jul 2026 12:47:58 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/learning-control-charts/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Learning%20Control%20Charts/cover%20image.webp" referrerpolicy="no-referrer">
            </div><p>The cover image was generated by ChatGPT.</p>
<p>While attending <a href="/en/the-35th-south-taiwan-statistics-conference" rel="">The 35th South Taiwan Statistics Conference</a>, I first learned about control charts from an experienced professor who had spent many years working in manufacturing environments. Intrigued by the concept, I decided to spend some time organizing my notes and gaining a foundational understanding of what control charts are.</p>]]></description></item><item><title>The Analysis of Characters in Dream of the Red Chamber</title><link>https://yao-chih.netlify.app/en/the-analysis-of-characters-in-dream-of-the-red-chamber/</link><pubDate>Tue, 23 Dec 2025 10:28:34 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/the-analysis-of-characters-in-dream-of-the-red-chamber/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/The%20Analysis%20of%20Characters%20in%20Dream%20of%20the%20Red%20Chamber/cover%20image.webp" referrerpolicy="no-referrer">
            &lt;/div>Classical Chinese novels are an indispensable and important part of Chinese literature, with many masterpieces throughout history worthy of in-depth exploration. Among them, Dream of the Red Chamber is regarded as one of the representative works of classical Chinese fiction and possesses a high degree of scholarly interest. The fates of the characters and the numerous events in the novel are intricately and closely interconnected, requiring thorough study to fully understand their true significance.</description></item><item><title>Proof of the Variance of the Sum of Two Random Variables</title><link>https://yao-chih.netlify.app/en/proof-of-the-variance-of-the-sum-of-two-random-variables/</link><pubDate>Sat, 04 Oct 2025 12:06:14 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/proof-of-the-variance-of-the-sum-of-two-random-variables/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Proof%20of%20the%20Variance%20of%20the%20Sum%20of%20Two%20Random%20Variables/cover%20image.webp" referrerpolicy="no-referrer">
            </div><p>This is a classic proof in mathematical statistics. That is, <strong>whether the variance of the sum of two random variables equals the sum of their variances</strong>.</p>]]></description></item><item><title>Logistic Regression</title><link>https://yao-chih.netlify.app/en/logistic-regression/</link><pubDate>Wed, 23 Jul 2025 10:55:24 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/logistic-regression/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Logistic%20Regression/cover%20image.webp" referrerpolicy="no-referrer">
            </div><p>The cover image was generated by ChatGPT.</p>
<h2 id="introduction">Introduction</h2>
<p><strong>Logistic regression</strong> is a type of binary classification model derived from <strong>linear regression</strong>. Unlike linear regression, which fits a regression line that passes through the data points by minimizing the sum of squared distances, known as the <strong>least squares method</strong>. The goal of logistic regression is to find a decision boundary that clearly separates the data into two distinct classes.</p>]]></description></item><item><title>Methods for Selecting Models</title><link>https://yao-chih.netlify.app/en/methods-for-selecting-models/</link><pubDate>Sun, 20 Jul 2025 11:55:51 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/methods-for-selecting-models/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Methods%20for%20Selecting%20Models/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div>How can we evaluate and compare multiple models, and select the best one for statistical analysis or machine learning? Simple models may fail to fit the data well, while overly complex models may suffer from "overfitting", reducing their predictive performance on future data.</description></item><item><title>Common Distance Measures</title><link>https://yao-chih.netlify.app/en/common-distance-measures/</link><pubDate>Fri, 20 Jun 2025 11:09:08 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/common-distance-measures/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Common%20Distance%20Measures/cover%20image.png" referrerpolicy="no-referrer">
            </div><p>Cover image generated by ChatGPT.</p>
<h2 id="introduction">Introduction</h2>
<p>How to calculate distance is an important aspect of modern statistical analysis. Choosing different distance metrics may lead to different results in statistical analysis methods. The following introduces commonly used distance metrics.</p>]]></description></item><item><title>Rectangular, Polar, and Spherical Coordinates</title><link>https://yao-chih.netlify.app/en/rectangular-polar-and-spherical-coordinates/</link><pubDate>Sun, 08 Jun 2025 14:57:34 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/rectangular-polar-and-spherical-coordinates/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/main/posts/Rectangular%20Polar%20and%20Spherical%20Coordinates/cover%20image.png" referrerpolicy="no-referrer">
            &lt;/div>Common coordinate systems include the rectangular coordinate system, the polar coordinate system, and the spherical coordinate system. Each has its own applications, advantages, and limitations.</description></item><item><title>Anscombe's Quartet</title><link>https://yao-chih.netlify.app/en/anscombes-quartet/</link><pubDate>Mon, 02 Jun 2025 21:40:50 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/anscombes-quartet/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/main/posts/Anscombe%27s%20quartet/cover%20image.png" referrerpolicy="no-referrer">
            </div><p>The cover image about Anscombe&rsquo;s Quartet was generated by ChatGPT, and it used the following prompt: &ldquo;The digital design highlights the title &lsquo;Anscombe&rsquo;s Quartet&rsquo; in bold, white sans-serif letters, centered against a dynamic abstract backdrop. The image is split into four colorful quadrants, each showcasing unique textures and patterns—ranging from painterly hues and curved lines to scattered circles and dots.&rdquo;</p>
<h2 id="introduction">Introduction</h2>
<p>While listening to a presentation today, I happened to hear a term——Anscombe&rsquo;s Quartet, a term I had never encountered before, yet it has significant implications in both statistics and data visualization.</p>]]></description></item></channel></rss>