From e647c399948e5d3d7fbb9d11bbf8cb1feb2de950 Mon Sep 17 00:00:00 2001
From: iago-pssjd <40892925+iago-pssjd@users.noreply.github.com>
Date: Wed, 26 Aug 2020 10:14:34 +0200
Subject: [PATCH 1/2] Update Rmd link

---
 index.Rmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/index.Rmd b/index.Rmd
index 9142b05..9a82704 100644
--- a/index.Rmd
+++ b/index.Rmd
@@ -1279,7 +1279,7 @@ Here are links to other sources who have exposed bits and pieces of this puzzle,
 # 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).
 
 

From 18a29111532ba9c3f365e9f26b9a820609d07235 Mon Sep 17 00:00:00 2001
From: iago-pssjd <40892925+iago-pssjd@users.noreply.github.com>
Date: Wed, 26 Aug 2020 10:15:40 +0200
Subject: [PATCH 2/2] Update html link

---
 index.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/index.html b/index.html
index 3ece0d1..895cc47 100644
--- a/index.html
+++ b/index.html
@@ -1629,7 +1629,7 @@ <h1><span class="header-section-number">8</span> Sources and further equivalence
 <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>