<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Statistical Inference on Everardo Shain</title><link>https://Everardo-shain.github.io/tags/statistical-inference/</link><description>Recent content in Statistical Inference on Everardo Shain</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Thu, 23 Nov 2023 08:06:25 +0600</lastBuildDate><atom:link href="https://Everardo-shain.github.io/tags/statistical-inference/index.xml" rel="self" type="application/rss+xml"/><item><title>US Children Adoption Statistical Inference</title><link>https://Everardo-shain.github.io/posts/projects/us-children-adoption-statistical-inference/</link><pubDate>Thu, 23 Nov 2023 08:06:25 +0600</pubDate><guid>https://Everardo-shain.github.io/posts/projects/us-children-adoption-statistical-inference/</guid><description>&lt;h2 id="overview"&gt;Overview&lt;/h2&gt;
&lt;p&gt;Project focused on understanding behaviors on the United States children adoption using a dataset from Centers for Disease Control and Prevention, where a total of 3 hypotheses were tested using R on RStudio. My team and I performed some data cleaning to avoid missing values and separate our variables of interest to them visualize them with bar plots and pie charts. For all the hypotheses we identified the Independent 2-group Mann-Whitney U Test as the best choice and performed it, then we reinforced our analysis by applying a parametric bootstrapping and power calculation where all 3 hypotheses got a good power greater than 80%.&lt;/p&gt;</description></item></channel></rss>