<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Population on Models for missing data</title><link>https://mtaniguchiking.github.io/M4MD-forecast-docs-dev/tags/population/</link><description>Recent content in Population on Models for missing data</description><generator>Hugo</generator><language>en-us</language><atom:link href="https://mtaniguchiking.github.io/M4MD-forecast-docs-dev/tags/population/index.xml" rel="self" type="application/rss+xml"/><item><title>Sampling and populations</title><link>https://mtaniguchiking.github.io/M4MD-forecast-docs-dev/posts/sampling-and-populations/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://mtaniguchiking.github.io/M4MD-forecast-docs-dev/posts/sampling-and-populations/</guid><description>&lt;p&gt;We sample for a very practical reason. It&amp;rsquo;s usually impossible to get information on the whole population, so we use a sample to make inferences about the population. In our case, the population is typically all sites in a stratum or all sites &amp;ndash; in all strata &amp;ndash; at the scale of an entire park. Typically, the inference we seek entails three questions.&lt;/p&gt;
&lt;ol&gt;
&lt;li&gt;What&amp;rsquo;s the best estimate of the population mean?&lt;/li&gt;
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&lt;blockquote&gt;
&lt;p&gt;We can generate a sample mean, 
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 \(\bar{x}\)
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, from our sample. This is the best estimate of the population mean.&lt;/p&gt;</description></item></channel></rss>