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 # Teaching materials and a course outline {#course}
 Most advanced stats books (and some intro-books) take the "everything is GLMM" approach as well. However, the "linear model" part often stays at the conceptual level, rather than being made explicit. I wanted to make linear models the *tool* in a concise way. Luckily, more beginner-friendly materials have emerged lately:
 
- * Russ Poldrack's open-source book "Statistical Thinking for the 21st century" (start at [chapter 5 on modeling](http://statsthinking21.org/fitting-models-to-data.html))
+ * Russ Poldrack's open-source book "Statistical Thinking for the 21st century" (start at [chapter 5 on modeling](https://statsthinking21.github.io/statsthinking21-core-site/fitting-models.html))
  * [Jeff Rouder's course notes](https://jeffrouder.blogspot.com/2019/03/teaching-undergrad-stats-without-p-f-or.html), introducing model comparison using just $R^2$ and BIC. It avoids all the jargon on p-values, F-values, etc. The full materials and slides [are available here](https://drive.google.com/drive/folders/1CiJK--bAuO0F-ug3B5I3FvmsCdpPGZ03).
 
 
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 <h1><span class="header-section-number">9</span> Teaching materials and a course outline</h1>
 <p>Most advanced stats books (and some intro-books) take the “everything is GLMM” approach as well. However, the “linear model” part often stays at the conceptual level, rather than being made explicit. I wanted to make linear models the <em>tool</em> in a concise way. Luckily, more beginner-friendly materials have emerged lately:</p>
 <ul>
-<li>Russ Poldrack’s open-source book “Statistical Thinking for the 21st century” (start at <a href="http://statsthinking21.org/fitting-models-to-data.html">chapter 5 on modeling</a>)</li>
+<li>Russ Poldrack’s open-source book “Statistical Thinking for the 21st century” (start at <a href="https://statsthinking21.github.io/statsthinking21-core-site/fitting-models.html">chapter 5 on modeling</a>)</li>
 <li><a href="https://jeffrouder.blogspot.com/2019/03/teaching-undergrad-stats-without-p-f-or.html">Jeff Rouder’s course notes</a>, introducing model comparison using just <span class="math inline">\(R^2\)</span> and BIC. It avoids all the jargon on p-values, F-values, etc. The full materials and slides <a href="https://drive.google.com/drive/folders/1CiJK--bAuO0F-ug3B5I3FvmsCdpPGZ03">are available here</a>.</li>
 </ul>
 <p>Here are my own thoughts on what I’d do. I’ve taught parts of this with great success already, but not the whole program since I’m not assigned to teach a full course yet.</p>