<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Rama Reksotinoyo</title><link>https://ramareksotinoyo.my.id/</link><description>Recent content on Rama Reksotinoyo</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 01 Jan 2024 00:00:00 +0000</lastBuildDate><atom:link href="https://ramareksotinoyo.my.id/index.xml" rel="self" type="application/rss+xml"/><item><title>Way to Avoid the Pitfall of Cherry-Picking Data</title><link>https://ramareksotinoyo.my.id/posts/avoid-cherry-picking-data/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://ramareksotinoyo.my.id/posts/avoid-cherry-picking-data/</guid><description>&lt;p&gt;I often cherry-pick samples from a population, even though there are many talented statisticians out there who have developed appropriate sampling methods for sampling needs. To avoid this fallacy, I am forced to learn about probability sampling.&lt;/p&gt;
&lt;p&gt;In this post, I&amp;rsquo;ll talk about implementing cluster sampling in C. Cluster sampling is one of those handy statistical sampling methods for pulling samples from a population. There are other cool probability sampling techniques too—like simple random sampling, stratified random sampling, and others—but let’s focus on cluster sampling for now.&lt;/p&gt;</description></item><item><title>CSFree — Download</title><link>https://ramareksotinoyo.my.id/csfree/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://ramareksotinoyo.my.id/csfree/</guid><description>Download CSFree</description></item><item><title>Projects</title><link>https://ramareksotinoyo.my.id/projects/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://ramareksotinoyo.my.id/projects/</guid><description>projects</description></item></channel></rss>