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<h1 class="title toc-ignore">What is Due & When?</h1>
<h3 class="subtitle">Spring 2022</h3>
<h4 class="author">Jubayer Hossain</h4>
</div>
<div id="readings" class="section level2">
<h2>Readings</h2>
<p>Readings will primarily be from two free, open-source, completely online textbooks:</p>
<ol style="list-style-type: decimal">
<li><a href="http://moderndive.netlify.com"><strong>MODERN DIVE into Data with R (MD)</strong></a></li>
<li><a href="http://r4ds.had.co.nz"><strong>R for Data Science (R4DS)</strong></a></li>
</ol>
<p>These books will be supplemented by blog posts, YouTube videos, and several open-access articles, including from:</p>
<ul>
<li>A special PEERJ issue called <a href="https://peerj.com/collections/50-practicaldatascistats/">“Practical Data Science for Stats”</a>, and</li>
<li><a href="https://www.nature.com/collections/qghhqm/pointsofsignificance">Nature: Points of Significance</a></li>
</ul>
</div>
<div id="materials" class="section level2">
<h2>Materials</h2>
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Exploratory Data Analysis<\/a><br><a href='http://r4ds.had.co.nz/workflow-projects.html'>- R4DS Ch 8: Workflow Projects<\/a>","<br><a href='http://r4ds.had.co.nz/data-import.html'>- R4DS Ch 11: Data Import<\/a><br><a href='http://r4ds.had.co.nz/relational-data.html'>- R4DS Ch 13: Relational Data<\/a><br><a href='https://peerj.com/preprints/3139/'>- PEERJ: Data for Collaboration<\/a><br><a href='https://peerj.com/preprints/3183/'>- PEERJ: Spreadsheets<\/a>","<a href='http://moderndive.netlify.com/6-regression.html'>- MD Ch 6: Basic Regression<\/a><br><a href = 'https://www.nature.com/articles/nmeth.3627'>- Nature POS: SLR<\/a>","<a href='http://moderndive.netlify.com/7-multiple-regression.html'>- MD Ch: Multiple Regression<\/a><br><a href = 'https://www.nature.com/articles/nmeth.3665'>- Nature POS: MLR<\/a>","<a href='https://www.nature.com/articles/nmeth.2613'>- Nature POS: Uncertainty<\/a><br><a href='https://www.nature.com/articles/nmeth.2659'>- Nature POS: Error bars<\/a><br><a href='https://statsbylopez.com/2015/08/31/regression-or-reversion/'>- Blog: Regression to the mean<\/a><br><a href='https://www.youtube.com/watch?time_continue=1&v=ebEkn-BiW5k'>- YouTube: Simpson's Paradox, Part 1<\/a><br><a href='https://www.youtube.com/watch?v=E_ME4P9fQbo'>- YouTube: Simpson's Paradox, Part 2<\/a>","<a href='https://www.markdowntutorial.com'>- Markdown Tutorial<\/a><br><a href='http://r4ds.had.co.nz/r-markdown.html'>- R4DS Ch 27: R Markdown<\/a><br><a href='http://r4ds.had.co.nz/factors.html'>- R4DS Ch 15: Factors<\/a>","<a href = 'https://www.nature.com/articles/nmeth.3812'>- Nature POS: Outliers<\/a><br><a href = 'https://www.nature.com/articles/nmeth.3854'>- Nature POS: Diagnostics<\/a>",null,"<a href='http://moderndive.netlify.com/8-sampling.html'>- MD Ch 8: Sampling<\/a>","<a href='http://moderndive.netlify.com/9-confidence-intervals'>- MD Ch 9: Confidence Intervals<\/a><br><a href='https://www.nature.com/articles/nmeth.3414\">- Nature POS: 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Exploratory Data Analysis Case Study (Ch 3 + 4)<\/a>","<a href='https://www.datacamp.com/courses/inference-for-numerical-data'>DC7: Inference for Numerical Data (Ch 1 + 2)<\/a>","<a href='https://www.datacamp.com/courses/inference-for-numerical-data'>DC7: Inference for Numerical Data (Ch 3 + 4)<\/a>","<a href='https://www.datacamp.com/courses/inference-for-categorical-data'>DC8: Inference for Categorical Data (Ch 1 + 2)<\/a>","<a href='https://www.datacamp.com/courses/inference-for-categorical-data'>DC8: Inference for Categorical Data (Ch 3 + 4)<\/a>","<a href='https://www.datacamp.com/courses/inference-for-linear-regression'>DC9: Inference for Linear Regression (Ch 1 + 2)<\/a>","<a href='https://www.datacamp.com/courses/inference-for-linear-regression'>DC9: Inference for Linear Regression (Ch 3 - 5)<\/a>","<a href='https://www.datacamp.com/courses/hierarchical-and-mixed-effects-models'>DC10: Hierarchical & Mixed Effects Models (Ch 1 + 2)<\/a>","<a 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