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	<title><![CDATA[BOL: Modern Statistics with R]]></title>
	<link>https://bioinformaticsonline.com/bookmarks/view/44648/modern-statistics-with-r?</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44648/modern-statistics-with-r</guid>
	<pubDate>Thu, 22 Aug 2024 04:44:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44648/modern-statistics-with-r</link>
	<title><![CDATA[Modern Statistics with R]]></title>
	<description><![CDATA[<p>This is the online version of the second edition of&nbsp;<em>Modern Statistics with R</em>. It is free to use, and always will be.&nbsp;<a href="https://www.routledge.com/Modern-Statistics-with-R-From-Wrangling-and-Exploring-Data-to-Inference-and-Predictive-Modelling/Thulin/p/book/9781032512440">Printed copies</a>&nbsp;are available from CRC Press.</p>
<p><span>Live&nbsp;<a href="https://statistikakademin.se/in-english-r/">online courses on statistics with R</a></span>&nbsp;based on this book, led by the author, are offered regularly; see&nbsp;<a href="https://statistikakademin.se/in-english-r/">this page</a>&nbsp;for more information and dates.</p>
<p>The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. The aim of&nbsp;<em>Modern Statistics with R</em>&nbsp;is to introduce you to key parts of the modern statistical toolkit. It teaches you:</p>
<ul>
<li><span>Data wrangling</span>&nbsp;- importing, formatting, reshaping, merging, and filtering data in R.</li>
<li><span>Exploratory data analysis</span>&nbsp;- using visualisations and multivariate techniques to explore datasets.</li>
<li><span>Statistical inference</span>&nbsp;- modern methods for testing hypotheses and computing confidence intervals.</li>
<li><span>Predictive modelling</span>&nbsp;- regression models and machine learning methods for prediction, classification, and forecasting.</li>
<li><span>Simulation</span>&nbsp;- using simulation techniques for sample size computations and evaluations of statistical methods.</li>
<li><span>Ethics in statistics</span>&nbsp;- ethical issues and good statistical practice.</li>
<li><span>R programming</span>&nbsp;- writing code that is fast, readable, and (hopefully!) free from bugs.</li>
</ul>
<p>The book includes plenty of examples and more than 200 exercises with worked solutions.&nbsp;<a href="http://www.modernstatisticswithr.com/data.zip">The datasets used for the examples and the exercises can be downloaded here.</a></p><p>Address of the bookmark: <a href="https://www.modernstatisticswithr.com/" rel="nofollow">https://www.modernstatisticswithr.com/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
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