diff --git a/01-IntroductionToPython.html b/01-IntroductionToPython.html
index 71470ce..2be3045 100644
--- a/01-IntroductionToPython.html
+++ b/01-IntroductionToPython.html
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="07-ResamplingAndSimulation.html">
-   Resampling and simulation in R
+   Resampling and simulation
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@@ -121,7 +121,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="13-GeneralLinearModel.html">
-   The General Linear Model in Python
+   The General Linear Model
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  </li>
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@@ -660,7 +660,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
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-<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>-0.7781456934580733
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+<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>array([ 93.87826084, 107.02436875,  87.70148102, 107.76083003,
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diff --git a/02-SummarizingData.html b/02-SummarizingData.html
index 3948d91..f02a9ad 100644
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   <a class="reference internal" href="07-ResamplingAndSimulation.html">
-   Resampling and simulation in R
+   Resampling and simulation
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="13-GeneralLinearModel.html">
-   The General Linear Model in Python
+   The General Linear Model
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diff --git a/03-DataVisualization.html b/03-DataVisualization.html
index 6eb2669..ecf3ed9 100644
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="07-ResamplingAndSimulation.html">
-   Resampling and simulation in R
+   Resampling and simulation
   </a>
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="13-GeneralLinearModel.html">
-   The General Linear Model in Python
+   The General Linear Model
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@@ -318,7 +318,7 @@ <h3>Bar vs. line plots<a class="headerlink" href="#bar-vs-line-plots" title="Per
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 <img alt="_images/03-DataVisualization_14_1.png" src="_images/03-DataVisualization_14_1.png" />
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 <img alt="_images/03-DataVisualization_16_1.png" src="_images/03-DataVisualization_16_1.png" />
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 <img alt="_images/03-DataVisualization_23_1.png" src="_images/03-DataVisualization_23_1.png" />
@@ -396,7 +396,7 @@ <h2>Plotting dispersion<a class="headerlink" href="#plotting-dispersion" title="
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@@ -428,8 +428,8 @@ <h3>Scatter plot<a class="headerlink" href="#scatter-plot" title="Permalink to t
                 <span class="n">data</span><span class="o">=</span><span class="n">adult_nhanes_data</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">adult_nhanes_data</span><span class="p">[</span><span class="s1">&#39;SystolicBloodPres1StRdgMmHg&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span>
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-          <span class="n">adult_nhanes_data</span><span class="p">[</span><span class="s1">&#39;SystolicBloodPres2NdRdgMmHg&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()],</span>
+          <span class="p">[</span><span class="n">adult_nhanes_data</span><span class="p">[</span><span class="s1">&#39;SystolicBloodPres1StRdgMmHg&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span>
+          <span class="n">adult_nhanes_data</span><span class="p">[</span><span class="s1">&#39;SystolicBloodPres1StRdgMmHg&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()],</span>
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 <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Systolic BP - Second reading&#39;</span><span class="p">)</span>
diff --git a/04-FittingSimpleModels.html b/04-FittingSimpleModels.html
index 6b9bd31..82d3656 100644
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  <li class="toctree-l1">
   <a class="reference internal" href="07-ResamplingAndSimulation.html">
-   Resampling and simulation in R
+   Resampling and simulation
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="13-GeneralLinearModel.html">
-   The General Linear Model in Python
+   The General Linear Model
   </a>
  </li>
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@@ -485,7 +485,7 @@ <h2>Variability<a class="headerlink" href="#variability" title="Permalink to thi
 </div>
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-<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>99.32116733780761
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@@ -559,10 +559,10 @@ <h2>Z-scores<a class="headerlink" href="#z-scores" title="Permalink to this head
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 </div>
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-<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>(array([10., 21., 15., 27., 16., 26.,  6., 15., 10.,  4.]),
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-         0.25560237,  0.66298745,  1.07037254,  1.47775763,  1.88514271,
-         2.2925278 ]),
+<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>(array([ 6., 15., 21., 29., 20., 21., 21., 10.,  5.,  2.]),
+ array([-2.05559782, -1.58611511, -1.1166324 , -0.64714969, -0.17766699,
+         0.29181572,  0.76129843,  1.23078114,  1.70026385,  2.16974655,
+         2.63922926]),
  &lt;a list of 10 Patch objects&gt;)
 </pre></div>
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index 73ab3a6..fa1e638 100644
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  <li class="toctree-l1">
   <a class="reference internal" href="07-ResamplingAndSimulation.html">
-   Resampling and simulation in R
+   Resampling and simulation
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="13-GeneralLinearModel.html">
-   The General Linear Model in Python
+   The General Linear Model
   </a>
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 </ul>
diff --git a/06-Sampling.html b/06-Sampling.html
index 76033b5..4b86ee9 100644
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     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
-    <link rel="next" title="Resampling and simulation in R" href="07-ResamplingAndSimulation.html" />
+    <link rel="next" title="Resampling and simulation" href="07-ResamplingAndSimulation.html" />
     <link rel="prev" title="Probability" href="05-Probability.html" />
 
     <meta name="viewport" content="width=device-width, initial-scale=1">
@@ -96,7 +96,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
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  <li class="toctree-l1">
   <a class="reference internal" href="07-ResamplingAndSimulation.html">
-   Resampling and simulation in R
+   Resampling and simulation
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@@ -121,7 +121,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="13-GeneralLinearModel.html">
-   The General Linear Model in Python
+   The General Linear Model
   </a>
  </li>
 </ul>
@@ -267,7 +267,7 @@ <h2>Sampling error<a class="headerlink" href="#sampling-error" title="Permalink
 <span class="c1"># we need to use the maximum of those data to set</span>
 <span class="c1"># the height of the vertical line that shows the mean</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">adult_nhanes_data</span><span class="p">[</span><span class="s1">&#39;Height&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span>
-            <span class="n">ymax</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">hist</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+            <span class="n">ymax</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
 
 <span class="c1"># draw the normal distribution with same mean and standard deviation</span>
 <span class="c1"># as the sampling distribution</span>
@@ -285,7 +285,7 @@ <h2>Sampling error<a class="headerlink" href="#sampling-error" title="Permalink
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7f55b4bc2f70&gt;]
+<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7f155446ee50&gt;]
 </pre></div>
 </div>
 <img alt="_images/06-Sampling_5_1.png" src="_images/06-Sampling_5_1.png" />
@@ -438,7 +438,7 @@ <h2>Confidence intervals<a class="headerlink" href="#confidence-intervals" title
   warnings.warn(
 </pre>
 </div>
-<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>0.945
+<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>0.958
 </pre></div>
 </div>
 </div>
@@ -457,7 +457,7 @@ <h2>Confidence intervals<a class="headerlink" href="#confidence-intervals" title
     <div class='prev-next-bottom'>
         
     <a class='left-prev' id="prev-link" href="05-Probability.html" title="previous page">Probability</a>
-    <a class='right-next' id="next-link" href="07-ResamplingAndSimulation.html" title="next page">Resampling and simulation in R</a>
+    <a class='right-next' id="next-link" href="07-ResamplingAndSimulation.html" title="next page">Resampling and simulation</a>
 
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diff --git a/07-ResamplingAndSimulation.html b/07-ResamplingAndSimulation.html
index f85e42d..4a97c19 100644
--- a/07-ResamplingAndSimulation.html
+++ b/07-ResamplingAndSimulation.html
@@ -5,7 +5,7 @@
 <html xmlns="http://www.w3.org/1999/xhtml">
   <head>
     <meta charset="utf-8" />
-    <title>Resampling and simulation in R &#8212; Python Companion to Statistical Thinking in the 21st Century</title>
+    <title>Resampling and simulation &#8212; Python Companion to Statistical Thinking in the 21st Century</title>
     <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.11.2/css/all.min.css" integrity="sha384-KA6wR/X5RY4zFAHpv/CnoG2UW1uogYfdnP67Uv7eULvTveboZJg0qUpmJZb5VqzN" crossorigin="anonymous">
     <link href="_static/css/index.css" rel="stylesheet">
     <link rel="stylesheet" href="_static/sphinx-book-theme.css" type="text/css" />
@@ -96,7 +96,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1 current active">
   <a class="current reference internal" href="#">
-   Resampling and simulation in R
+   Resampling and simulation
   </a>
  </li>
  <li class="toctree-l1">
@@ -121,7 +121,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="13-GeneralLinearModel.html">
-   The General Linear Model in Python
+   The General Linear Model
   </a>
  </li>
 </ul>
@@ -215,8 +215,8 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
         
               <div>
                 
-  <div class="section" id="resampling-and-simulation-in-r">
-<h1>Resampling and simulation in R<a class="headerlink" href="#resampling-and-simulation-in-r" title="Permalink to this headline">¶</a></h1>
+  <div class="section" id="resampling-and-simulation">
+<h1>Resampling and simulation<a class="headerlink" href="#resampling-and-simulation" title="Permalink to this headline">¶</a></h1>
 <div class="section" id="generating-random-samples">
 <h2>Generating random samples<a class="headerlink" href="#generating-random-samples" title="Permalink to this headline">¶</a></h2>
 <p>Here we will generate random samples from a number of different distributions and plot their histograms.  We could write out separate commands to plot each of our functions of interest, but that would involve repeating a lot of code, so instead we will take advantage of the fact that Python allows us to treat modules as variables.  We will specify the module that creates each distribution, and then loop through them, each time incrementing the panel number.  Some distributions also take specific parameters; for example, the Chi-squared distribution requires specifying the degrees of freedom.  We will store those in a separate dictionary and use them as needed.</p>
@@ -292,7 +292,7 @@ <h2>Simulating the maximum finishing time<a class="headerlink" href="#simulating
 </div>
 </div>
 </div>
-<p>Now let’s find the 99th percentile of the maximum distriibution.  There is a built-in function in the <code class="docutils literal notranslate"><span class="pre">scipy.stats</span></code> module, called <code class="docutils literal notranslate"><span class="pre">scoreatpercentile</span></code> that will do this for us:</p>
+<p>Now let’s find the 95th percentile of the maximum distriibution.  There is a built-in function in the <code class="docutils literal notranslate"><span class="pre">scipy.stats</span></code> module, called <code class="docutils literal notranslate"><span class="pre">scoreatpercentile</span></code> that will do this for us:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cutoff</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">scoreatpercentile</span><span class="p">(</span><span class="n">sample_max_df</span><span class="p">[</span><span class="s1">&#39;max&#39;</span><span class="p">],</span> <span class="mi">95</span><span class="p">)</span>
diff --git a/08-HypothesisTesting.html b/08-HypothesisTesting.html
index 1d41c28..d91507c 100644
--- a/08-HypothesisTesting.html
+++ b/08-HypothesisTesting.html
@@ -32,7 +32,7 @@
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
     <link rel="next" title="Statistical Power Analysis in Python" href="09-StatisticalPower.html" />
-    <link rel="prev" title="Resampling and simulation in R" href="07-ResamplingAndSimulation.html" />
+    <link rel="prev" title="Resampling and simulation" href="07-ResamplingAndSimulation.html" />
 
     <meta name="viewport" content="width=device-width, initial-scale=1">
     <meta name="docsearch:language" content="en">
@@ -96,7 +96,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="07-ResamplingAndSimulation.html">
-   Resampling and simulation in R
+   Resampling and simulation
   </a>
  </li>
  <li class="toctree-l1 current active">
@@ -121,7 +121,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="13-GeneralLinearModel.html">
-   The General Linear Model in Python
+   The General Linear Model
   </a>
  </li>
 </ul>
@@ -248,13 +248,13 @@ <h2>Simple example: Coin-flipping<a class="headerlink" href="#simple-example-coi
 <div class="cell_input docutils container">
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">scipy.stats</span>
 
-<span class="n">pvalue</span> <span class="o">=</span> <span class="mi">100</span> <span class="o">-</span> <span class="n">scipy</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">percentileofscore</span><span class="p">(</span><span class="n">flip_results_df</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">)</span> 
+<span class="n">pvalue</span> <span class="o">=</span> <span class="mi">100</span> <span class="o">-</span> <span class="n">scipy</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">percentileofscore</span><span class="p">(</span><span class="n">flip_results_df</span><span class="p">,</span> <span class="mi">70</span><span class="p">)</span> 
 <span class="n">pvalue</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>100.0
+<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>0.010000000000005116
 </pre></div>
 </div>
 </div>
@@ -319,14 +319,14 @@ <h2>Simulating p-values<a class="headerlink" href="#simulating-p-values" title="
 <span class="k">for</span> <span class="n">run</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_runs</span><span class="p">):</span>
     <span class="n">sim_results_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">run</span><span class="p">,</span> <span class="s1">&#39;p_value&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sample_ttest</span><span class="p">()</span>
 
-<span class="n">p_error</span> <span class="o">=</span> <span class="n">sim_results_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sim_results_df</span><span class="p">[</span><span class="s1">&#39;p_value&#39;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">0.05</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">p_error</span> <span class="o">=</span> <span class="n">sim_results_df</span><span class="p">[</span><span class="s1">&#39;p_value&#39;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">0.05</span>
+<span class="n">p_error</span> <span class="o">=</span> <span class="n">p_error</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="n">p_error</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>p_value    0.024385
-dtype: float64
+<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>0.0512
 </pre></div>
 </div>
 </div>
@@ -344,7 +344,7 @@ <h2>Simulating p-values<a class="headerlink" href="#simulating-p-values" title="
     
     <div class='prev-next-bottom'>
         
-    <a class='left-prev' id="prev-link" href="07-ResamplingAndSimulation.html" title="previous page">Resampling and simulation in R</a>
+    <a class='left-prev' id="prev-link" href="07-ResamplingAndSimulation.html" title="previous page">Resampling and simulation</a>
     <a class='right-next' id="next-link" href="09-StatisticalPower.html" title="next page">Statistical Power Analysis in Python</a>
 
     </div>
diff --git a/09-StatisticalPower.html b/09-StatisticalPower.html
index 8a18a77..cbdc848 100644
--- a/09-StatisticalPower.html
+++ b/09-StatisticalPower.html
@@ -96,7 +96,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="07-ResamplingAndSimulation.html">
-   Resampling and simulation in R
+   Resampling and simulation
   </a>
  </li>
  <li class="toctree-l1">
@@ -121,7 +121,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="13-GeneralLinearModel.html">
-   The General Linear Model in Python
+   The General Linear Model
   </a>
  </li>
 </ul>
@@ -237,7 +237,7 @@ <h1>Statistical Power Analysis in Python<a class="headerlink" href="#statistical
 </div>
 <div class="section" id="power-analysis">
 <h2>Power analysis<a class="headerlink" href="#power-analysis" title="Permalink to this headline">¶</a></h2>
-<p>We can compute a power analysis using functions from the <code class="docutils literal notranslate"><span class="pre">statsmodels.stats.power</span></code> package. Let’s focus on the power for an independent samples t-test in order to determine a difference in the mean between two groups.  Let’s say that we think than an effect size of Cohen’s d=0.5 is realistic for the study in question (based on previous research) and would be of scientific interest.  We wish to have 80% power to find the effect if it exists.  We can compute the sample size needed for adequate power using the <code class="docutils literal notranslate"><span class="pre">TTestIndPower()</span></code> function:</p>
+<p>We can compute a power analysis using functions from the <code class="docutils literal notranslate"><span class="pre">statsmodels.stats.power</span></code> package. Let’s focus on the power for an independent samples t-test in order to determine a difference in the mean between two groups.  Let’s say that we think that an effect size of Cohen’s d=0.5 is realistic for the study in question (based on previous research) and would be of scientific interest.  We wish to have 80% power to find the effect if it exists.  We can compute the sample size needed for adequate power using the <code class="docutils literal notranslate"><span class="pre">TTestIndPower()</span></code> function:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span>
diff --git a/10-BayesianStatistics.html b/10-BayesianStatistics.html
index f624e71..23b99b1 100644
--- a/10-BayesianStatistics.html
+++ b/10-BayesianStatistics.html
@@ -96,7 +96,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="07-ResamplingAndSimulation.html">
-   Resampling and simulation in R
+   Resampling and simulation
   </a>
  </li>
  <li class="toctree-l1">
@@ -121,7 +121,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="13-GeneralLinearModel.html">
-   The General Linear Model in Python
+   The General Linear Model
   </a>
  </li>
 </ul>
@@ -221,7 +221,7 @@ <h1>Bayesian Statistics in Python<a class="headerlink" href="#bayesian-statistic
 <div class="section" id="applying-bayes-theorem-a-simple-example">
 <h2>Applying Bayes’ theorem: A simple example<a class="headerlink" href="#applying-bayes-theorem-a-simple-example" title="Permalink to this headline">¶</a></h2>
 <p>TBD: MOVE TO MULTIPLE TESTING EXAMPLE SO WE CAN USE BINOMIAL LIKELIHOOD
-A person has a cough and flu-like symptoms, and gets a PCR test for COVID-19, which comes back postiive.  What is the likelihood that they actually have COVID-19, as opposed a regular cold or flu?  We can use Bayes’ theorem to compute this.  Let’s say that the local rate of symptomatic individuals who actually are infected with COVID-19 is 7.4% (as <a class="reference external" href="https://twitter.com/Bob_Wachter/status/1281792549309386752/photo/1">reported</a> on July 10, 2020 for San Francisco); thus, our prior probability that someone with symptoms actually has COVID-19 is .074.  The RT-PCR test used to identify COVID-19 RNA is highly specific (that is, it very rarelly reports the presence of the virus when it is not present); for our example, we will say that the specificity is 99%.  Its sensitivity is not known, but probably is no higher than 90%.<br />
+A person has a cough and flu-like symptoms, and gets a PCR test for COVID-19, which comes back postiive.  What is the likelihood that they actually have COVID-19, as opposed to a regular cold or flu?  We can use Bayes’ theorem to compute this.  Let’s say that the local rate of symptomatic individuals who actually are infected with COVID-19 is 7.4% (as <a class="reference external" href="https://twitter.com/Bob_Wachter/status/1281792549309386752/photo/1">reported</a> on July 10, 2020 for San Francisco); thus, our prior probability that someone with symptoms actually has COVID-19 is .074.  The RT-PCR test used to identify COVID-19 RNA is highly specific (that is, it very rarelly reports the presence of the virus when it is not present); for our example, we will say that the specificity is 99%.  Its sensitivity is not known, but probably is no higher than 90%.<br />
 First let’s look at the probability of disease given a single positive test.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -234,6 +234,8 @@ <h2>Applying Bayes’ theorem: A simple example<a class="headerlink" href="#appl
 <span class="n">posterior</span> <span class="o">=</span> <span class="p">(</span><span class="n">likelihood</span> <span class="o">*</span> <span class="n">prior</span><span class="p">)</span> <span class="o">/</span> <span class="n">marginal_likelihood</span>
 <span class="n">posterior</span>
 
+
+
 </pre></div>
 </div>
 </div>
@@ -316,7 +318,7 @@ <h2>Estimating posterior distributions<a class="headerlink" href="#estimating-po
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend at 0x7f4d3cdf0b80&gt;
+<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend at 0x7f1ad373bb80&gt;
 </pre></div>
 </div>
 <img alt="_images/10-BayesianStatistics_5_1.png" src="_images/10-BayesianStatistics_5_1.png" />
@@ -360,7 +362,7 @@ <h2>Estimating posterior distributions<a class="headerlink" href="#estimating-po
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend at 0x7f4d3cd73b80&gt;
+<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend at 0x7f1ad36bbb80&gt;
 </pre></div>
 </div>
 <img alt="_images/10-BayesianStatistics_7_1.png" src="_images/10-BayesianStatistics_7_1.png" />
diff --git a/11-ModelingCategoricalRelationships.html b/11-ModelingCategoricalRelationships.html
index 22862cb..4228013 100644
--- a/11-ModelingCategoricalRelationships.html
+++ b/11-ModelingCategoricalRelationships.html
@@ -31,7 +31,7 @@
     <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
-    <link rel="next" title="The General Linear Model in Python" href="13-GeneralLinearModel.html" />
+    <link rel="next" title="The General Linear Model" href="13-GeneralLinearModel.html" />
     <link rel="prev" title="Bayesian Statistics in Python" href="10-BayesianStatistics.html" />
 
     <meta name="viewport" content="width=device-width, initial-scale=1">
@@ -96,7 +96,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="07-ResamplingAndSimulation.html">
-   Resampling and simulation in R
+   Resampling and simulation
   </a>
  </li>
  <li class="toctree-l1">
@@ -121,7 +121,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="13-GeneralLinearModel.html">
-   The General Linear Model in Python
+   The General Linear Model
   </a>
  </li>
 </ul>
@@ -430,7 +430,7 @@ <h2>The Pearson Chi-squared test<a class="headerlink" href="#the-pearson-chi-squ
     <div class='prev-next-bottom'>
         
     <a class='left-prev' id="prev-link" href="10-BayesianStatistics.html" title="previous page">Bayesian Statistics in Python</a>
-    <a class='right-next' id="next-link" href="13-GeneralLinearModel.html" title="next page">The General Linear Model in Python</a>
+    <a class='right-next' id="next-link" href="13-GeneralLinearModel.html" title="next page">The General Linear Model</a>
 
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diff --git a/13-GeneralLinearModel.html b/13-GeneralLinearModel.html
index 71aef47..6bc52dd 100644
--- a/13-GeneralLinearModel.html
+++ b/13-GeneralLinearModel.html
@@ -5,7 +5,7 @@
 <html xmlns="http://www.w3.org/1999/xhtml">
   <head>
     <meta charset="utf-8" />
-    <title>The General Linear Model in Python &#8212; Python Companion to Statistical Thinking in the 21st Century</title>
+    <title>The General Linear Model &#8212; Python Companion to Statistical Thinking in the 21st Century</title>
     <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.11.2/css/all.min.css" integrity="sha384-KA6wR/X5RY4zFAHpv/CnoG2UW1uogYfdnP67Uv7eULvTveboZJg0qUpmJZb5VqzN" crossorigin="anonymous">
     <link href="_static/css/index.css" rel="stylesheet">
     <link rel="stylesheet" href="_static/sphinx-book-theme.css" type="text/css" />
@@ -95,7 +95,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="07-ResamplingAndSimulation.html">
-   Resampling and simulation in R
+   Resampling and simulation
   </a>
  </li>
  <li class="toctree-l1">
@@ -120,7 +120,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1 current active">
   <a class="current reference internal" href="#">
-   The General Linear Model in Python
+   The General Linear Model
   </a>
  </li>
 </ul>
@@ -224,8 +224,8 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
         
               <div>
                 
-  <div class="section" id="the-general-linear-model-in-python">
-<h1>The General Linear Model in Python<a class="headerlink" href="#the-general-linear-model-in-python" title="Permalink to this headline">¶</a></h1>
+  <div class="section" id="the-general-linear-model">
+<h1>The General Linear Model<a class="headerlink" href="#the-general-linear-model" title="Permalink to this headline">¶</a></h1>
 <p>In this chapter we will explore how to fit general linear models in Python.  We will focus on the tools provided by the <code class="docutils literal notranslate"><span class="pre">statsmodels</span></code> package.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -280,7 +280,7 @@ <h2>Linear regression<a class="headerlink" href="#linear-regression" title="Perm
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7f67d5822a00&gt;
+<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7fe809e679a0&gt;
 </pre></div>
 </div>
 <img alt="_images/13-GeneralLinearModel_3_1.png" src="_images/13-GeneralLinearModel_3_1.png" />
@@ -313,7 +313,7 @@ <h2>Linear regression<a class="headerlink" href="#linear-regression" title="Perm
   <th>Date:</th>             <td>Tue, 31 Aug 2021</td> <th>  Prob (F-statistic):</th> <td>2.20e-17</td>
 </tr>
 <tr>
-  <th>Time:</th>                 <td>14:51:50</td>     <th>  Log-Likelihood:    </th> <td> -134.44</td>
+  <th>Time:</th>                 <td>14:59:55</td>     <th>  Log-Likelihood:    </th> <td> -134.44</td>
 </tr>
 <tr>
   <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th> <td>   272.9</td>
@@ -382,55 +382,12 @@ <h2>Model criticism and diagnostics<a class="headerlink" href="#model-criticism-
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
 <span class="kn">import</span> <span class="nn">scipy.stats</span>
 
-<span class="n">scipy</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">probplot</span><span class="p">(</span><span class="n">ols_result</span><span class="o">.</span><span class="n">resid</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="n">sns</span><span class="o">.</span><span class="n">mpl</span><span class="o">.</span><span class="n">pyplot</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">probplot</span><span class="p">(</span><span class="n">ols_result</span><span class="o">.</span><span class="n">resid</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="n">sns</span><span class="o">.</span><span class="n">mpl</span><span class="o">.</span><span class="n">pyplot</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>((array([-2.46203784, -2.12570747, -1.93122778, -1.79044653, -1.67819304,
-         -1.58381122, -1.50174123, -1.42869743, -1.36256869, -1.30191411,
-         -1.24570419, -1.19317644, -1.14374949, -1.09696931, -1.05247413,
-         -1.00997067, -0.96921765, -0.93001393, -0.89218993, -0.85560121,
-         -0.82012357, -0.78564937, -0.75208458, -0.71934648, -0.68736185,
-         -0.65606548, -0.62539893, -0.59530962, -0.56574992, -0.53667655,
-         -0.50804994, -0.47983378, -0.45199463, -0.42450149, -0.39732558,
-         -0.37044003, -0.34381966, -0.31744076, -0.29128096, -0.26531902,
-         -0.23953472, -0.21390872, -0.18842244, -0.16305799, -0.13779803,
-         -0.1126257 , -0.08752455, -0.06247843, -0.03747145, -0.01248789,
-          0.01248789,  0.03747145,  0.06247843,  0.08752455,  0.1126257 ,
-          0.13779803,  0.16305799,  0.18842244,  0.21390872,  0.23953472,
-          0.26531902,  0.29128096,  0.31744076,  0.34381966,  0.37044003,
-          0.39732558,  0.42450149,  0.45199463,  0.47983378,  0.50804994,
-          0.53667655,  0.56574992,  0.59530962,  0.62539893,  0.65606548,
-          0.68736185,  0.71934648,  0.75208458,  0.78564937,  0.82012357,
-          0.85560121,  0.89218993,  0.93001393,  0.96921765,  1.00997067,
-          1.05247413,  1.09696931,  1.14374949,  1.19317644,  1.24570419,
-          1.30191411,  1.36256869,  1.42869743,  1.50174123,  1.58381122,
-          1.67819304,  1.79044653,  1.93122778,  2.12570747,  2.46203784]),
-  array([-2.5482371 , -2.0909615 , -1.91011081, -1.78183221, -1.67901437,
-         -1.65965562, -1.39835615, -1.35433747, -1.35349874, -1.32450874,
-         -1.32071777, -1.31372454, -1.31149674, -1.26359797, -1.03141285,
-         -1.02807434, -0.96988026, -0.77573225, -0.76933571, -0.74914872,
-         -0.71307311, -0.65612258, -0.59211795, -0.53953167, -0.51333264,
-         -0.4857747 , -0.4792586 , -0.44367702, -0.41520854, -0.36352006,
-         -0.35492012, -0.32790965, -0.31527698, -0.3145051 , -0.30314853,
-         -0.27638373, -0.26193063, -0.18035491, -0.17125738, -0.16925471,
-         -0.16405551, -0.1568569 , -0.13346467, -0.09060228, -0.08741092,
-         -0.07851139, -0.06660155,  0.05147435,  0.05283821,  0.05769373,
-          0.07401461,  0.09534008,  0.12202649,  0.18569196,  0.20481024,
-          0.20627062,  0.24199814,  0.26910835,  0.2709454 ,  0.28029596,
-          0.30157991,  0.30688003,  0.31428509,  0.33836826,  0.36473919,
-          0.37760336,  0.42704054,  0.44189202,  0.47162341,  0.47663995,
-          0.47974695,  0.48018453,  0.48417952,  0.48811872,  0.49645734,
-          0.51109903,  0.54895531,  0.66572149,  0.70984843,  0.72310305,
-          0.72991447,  0.73133351,  0.7931718 ,  0.80261309,  0.83081737,
-          0.89891574,  0.97037584,  1.00083831,  1.02499538,  1.04070618,
-          1.1070823 ,  1.13587234,  1.15439669,  1.21366375,  1.49236429,
-          1.77213663,  1.79345755,  1.835123  ,  2.07330289,  2.3660403 ])),
- (0.9415840543542681, -1.5528172622144374e-15, 0.9941442250040646))
-</pre></div>
-</div>
-<img alt="_images/13-GeneralLinearModel_9_1.png" src="_images/13-GeneralLinearModel_9_1.png" />
+<img alt="_images/13-GeneralLinearModel_9_0.png" src="_images/13-GeneralLinearModel_9_0.png" />
 </div>
 </div>
 <p>This looks pretty good, in the sense that the residual data points fall very close to the unit line.  This is not surprising, since we generated the data with normally distributed noise.  We should also plot the predicted (or <em>fitted</em>) values against the residuals, to make sure that the model does work systematically better for some predicted values versus others.</p>
@@ -499,7 +456,7 @@ <h2>Examples of problematic model fit<a class="headerlink" href="#examples-of-pr
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7f67cd0f35b0&gt;
+<div class="output text_plain highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7fe801962b50&gt;
 </pre></div>
 </div>
 <img alt="_images/13-GeneralLinearModel_17_1.png" src="_images/13-GeneralLinearModel_17_1.png" />
@@ -529,7 +486,7 @@ <h2>Examples of problematic model fit<a class="headerlink" href="#examples-of-pr
   <th>Date:</th>             <td>Tue, 31 Aug 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.363</td> 
 </tr>
 <tr>
-  <th>Time:</th>                 <td>14:51:51</td>     <th>  Log-Likelihood:    </th> <td> -149.89</td>
+  <th>Time:</th>                 <td>14:59:56</td>     <th>  Log-Likelihood:    </th> <td> -149.89</td>
 </tr>
 <tr>
   <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th> <td>   303.8</td>
@@ -605,7 +562,7 @@ <h2>Examples of problematic model fit<a class="headerlink" href="#examples-of-pr
   <th>Date:</th>             <td>Tue, 31 Aug 2021</td> <th>  Prob (F-statistic):</th> <td>4.87e-105</td>
 </tr>
 <tr>
-  <th>Time:</th>                 <td>14:51:51</td>     <th>  Log-Likelihood:    </th> <td>  92.204</td> 
+  <th>Time:</th>                 <td>14:59:56</td>     <th>  Log-Likelihood:    </th> <td>  92.204</td> 
 </tr>
 <tr>
   <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th> <td>  -180.4</td> 
@@ -711,7 +668,7 @@ <h2>Extending regression to binary outcomes.<a class="headerlink" href="#extendi
   <th>Date:</th>            <td>Tue, 31 Aug 2021</td> <th>  Pseudo R-squ.:     </th>   <td>0.1661</td>  
 </tr>
 <tr>
-  <th>Time:</th>                <td>14:51:51</td>     <th>  Log-Likelihood:    </th>  <td> -1895.1</td> 
+  <th>Time:</th>                <td>14:59:56</td>     <th>  Log-Likelihood:    </th>  <td> -1895.1</td> 
 </tr>
 <tr>
   <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th>  <td> -2272.6</td> 
diff --git a/_images/03-DataVisualization_23_1.png b/_images/03-DataVisualization_23_1.png
index f3705e9..171f87d 100644
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diff --git a/_images/04-FittingSimpleModels_46_1.png b/_images/04-FittingSimpleModels_46_1.png
index a0dd65a..7cd0d31 100644
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index f82d601..08ae167 100644
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diff --git a/_images/06-Sampling_11_0.png b/_images/06-Sampling_11_0.png
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diff --git a/_images/06-Sampling_13_1.png b/_images/06-Sampling_13_1.png
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diff --git a/_images/06-Sampling_5_1.png b/_images/06-Sampling_5_1.png
index 1ea6452..31e3fb4 100644
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diff --git a/_images/07-ResamplingAndSimulation_1_0.png b/_images/07-ResamplingAndSimulation_1_0.png
index 60a2e65..6b33008 100644
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diff --git a/_images/07-ResamplingAndSimulation_7_0.png b/_images/07-ResamplingAndSimulation_7_0.png
index a6359fc..d6ef586 100644
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diff --git a/_images/13-GeneralLinearModel_9_1.png b/_images/13-GeneralLinearModel_9_0.png
similarity index 100%
rename from _images/13-GeneralLinearModel_9_1.png
rename to _images/13-GeneralLinearModel_9_0.png
diff --git a/_sources/03-DataVisualization.ipynb b/_sources/03-DataVisualization.ipynb
index f68ca08..6df626b 100644
--- a/_sources/03-DataVisualization.ipynb
+++ b/_sources/03-DataVisualization.ipynb
@@ -387,8 +387,8 @@
     "                data=adult_nhanes_data)\n",
     "plt.plot([adult_nhanes_data['SystolicBloodPres1StRdgMmHg'].min(),\n",
     "          adult_nhanes_data['SystolicBloodPres1StRdgMmHg'].max()],\n",
-    "          [adult_nhanes_data['SystolicBloodPres2NdRdgMmHg'].min(),\n",
-    "          adult_nhanes_data['SystolicBloodPres2NdRdgMmHg'].max()],\n",
+    "          [adult_nhanes_data['SystolicBloodPres1StRdgMmHg'].min(),\n",
+    "          adult_nhanes_data['SystolicBloodPres1StRdgMmHg'].max()],\n",
     "          color='k')\n",
     "plt.xlabel('Systolic BP - First reading')\n",
     "plt.ylabel('Systolic BP - Second reading')"
diff --git a/_sources/06-Sampling.ipynb b/_sources/06-Sampling.ipynb
index 8011dd4..7223dd8 100644
--- a/_sources/06-Sampling.ipynb
+++ b/_sources/06-Sampling.ipynb
@@ -79,7 +79,7 @@
     "# we need to use the maximum of those data to set\n",
     "# the height of the vertical line that shows the mean\n",
     "plt.axvline(x=adult_nhanes_data['Height'].mean(),\n",
-    "            ymax=np.max(hist[0]), color='k')\n",
+    "            ymax=1, color='k')\n",
     "\n",
     "# draw the normal distribution with same mean and standard deviation\n",
     "# as the sampling distribution\n",
diff --git a/_sources/07-ResamplingAndSimulation.ipynb b/_sources/07-ResamplingAndSimulation.ipynb
index fe6122d..6605cca 100644
--- a/_sources/07-ResamplingAndSimulation.ipynb
+++ b/_sources/07-ResamplingAndSimulation.ipynb
@@ -4,7 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Resampling and simulation in R\n",
+
+    "# Resampling and simulation\n",
     "\n",
     "## Generating random samples\n",
     "Here we will generate random samples from a number of different distributions and plot their histograms.  We could write out separate commands to plot each of our functions of interest, but that would involve repeating a lot of code, so instead we will take advantage of the fact that Python allows us to treat modules as variables.  We will specify the module that creates each distribution, and then loop through them, each time incrementing the panel number.  Some distributions also take specific parameters; for example, the Chi-squared distribution requires specifying the degrees of freedom.  We will store those in a separate dictionary and use them as needed."
@@ -97,7 +98,7 @@
     "lines_to_next_cell": 2
    },
    "source": [
-    "Now let's find the 99th percentile of the maximum distriibution.  There is a built-in function in the `scipy.stats` module, called `scoreatpercentile` that will do this for us:\n"
+    "Now let's find the 95th percentile of the maximum distriibution.  There is a built-in function in the `scipy.stats` module, called `scoreatpercentile` that will do this for us:\n"
    ]
   },
   {
diff --git a/_sources/08-HypothesisTesting.ipynb b/_sources/08-HypothesisTesting.ipynb
index e0c86c1..a908050 100644
--- a/_sources/08-HypothesisTesting.ipynb
+++ b/_sources/08-HypothesisTesting.ipynb
@@ -57,7 +57,7 @@
    "source": [
     "import scipy.stats\n",
     "\n",
-    "pvalue = 100 - scipy.stats.percentileofscore(flip_results_df, 0.7) \n",
+    "pvalue = 100 - scipy.stats.percentileofscore(flip_results_df, 70) \n",
     "pvalue"
    ]
   },
@@ -144,7 +144,8 @@
     "for run in range(num_runs):\n",
     "    sim_results_df.loc[run, 'p_value'] = sample_ttest()\n",
     "\n",
-    "p_error = sim_results_df.loc[sim_results_df['p_value'] < 0.05].mean(axis=0)\n",
+    "p_error = sim_results_df['p_value'] < 0.05\n",
+    "p_error = p_error.mean(axis=0)\n",
     "p_error"
    ]
   },
diff --git a/_sources/09-StatisticalPower.ipynb b/_sources/09-StatisticalPower.ipynb
index 34040ad..77d41f7 100644
--- a/_sources/09-StatisticalPower.ipynb
+++ b/_sources/09-StatisticalPower.ipynb
@@ -37,7 +37,7 @@
    "source": [
     "## Power analysis\n",
     "\n",
-    "We can compute a power analysis using functions from the `statsmodels.stats.power` package. Let's focus on the power for an independent samples t-test in order to determine a difference in the mean between two groups.  Let's say that we think than an effect size of Cohen's d=0.5 is realistic for the study in question (based on previous research) and would be of scientific interest.  We wish to have 80% power to find the effect if it exists.  We can compute the sample size needed for adequate power using the `TTestIndPower()` function:"
+    "We can compute a power analysis using functions from the `statsmodels.stats.power` package. Let's focus on the power for an independent samples t-test in order to determine a difference in the mean between two groups.  Let's say that we think that an effect size of Cohen's d=0.5 is realistic for the study in question (based on previous research) and would be of scientific interest.  We wish to have 80% power to find the effect if it exists.  We can compute the sample size needed for adequate power using the `TTestIndPower()` function:"
    ]
   },
   {
diff --git a/_sources/10-BayesianStatistics.ipynb b/_sources/10-BayesianStatistics.ipynb
index 420991d..e7ae032 100644
--- a/_sources/10-BayesianStatistics.ipynb
+++ b/_sources/10-BayesianStatistics.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "## Applying Bayes' theorem: A simple example\n",
     "TBD: MOVE TO MULTIPLE TESTING EXAMPLE SO WE CAN USE BINOMIAL LIKELIHOOD\n",
-    "A person has a cough and flu-like symptoms, and gets a PCR test for COVID-19, which comes back postiive.  What is the likelihood that they actually have COVID-19, as opposed a regular cold or flu?  We can use Bayes' theorem to compute this.  Let's say that the local rate of symptomatic individuals who actually are infected with COVID-19 is 7.4% (as [reported](https://twitter.com/Bob_Wachter/status/1281792549309386752/photo/1) on July 10, 2020 for San Francisco); thus, our prior probability that someone with symptoms actually has COVID-19 is .074.  The RT-PCR test used to identify COVID-19 RNA is highly specific (that is, it very rarelly reports the presence of the virus when it is not present); for our example, we will say that the specificity is 99%.  Its sensitivity is not known, but probably is no higher than 90%.  \n",
+    "A person has a cough and flu-like symptoms, and gets a PCR test for COVID-19, which comes back postiive.  What is the likelihood that they actually have COVID-19, as opposed to a regular cold or flu?  We can use Bayes' theorem to compute this.  Let's say that the local rate of symptomatic individuals who actually are infected with COVID-19 is 7.4% (as [reported](https://twitter.com/Bob_Wachter/status/1281792549309386752/photo/1) on July 10, 2020 for San Francisco); thus, our prior probability that someone with symptoms actually has COVID-19 is .074.  The RT-PCR test used to identify COVID-19 RNA is highly specific (that is, it very rarelly reports the presence of the virus when it is not present); for our example, we will say that the specificity is 99%.  Its sensitivity is not known, but probably is no higher than 90%.  \n",
     "First let's look at the probability of disease given a single positive test."
    ]
   },
@@ -29,6 +29,8 @@
     "marginal_likelihood = sensitivity * prior + (1 - specificity) * (1 - prior)\n",
     "posterior = (likelihood * prior) / marginal_likelihood\n",
     "posterior\n",
+    "\n",
+    "\n",
     "\n"
    ]
   },
diff --git a/_sources/13-GeneralLinearModel.ipynb b/_sources/13-GeneralLinearModel.ipynb
index a7d39bc..8768920 100644
--- a/_sources/13-GeneralLinearModel.ipynb
+++ b/_sources/13-GeneralLinearModel.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# The General Linear Model in Python\n",
+    "# The General Linear Model\n",
     "In this chapter we will explore how to fit general linear models in Python.  We will focus on the tools provided by the `statsmodels` package."
    ]
   },
@@ -134,7 +134,7 @@
     "import seaborn as sns\n",
     "import scipy.stats\n",
     "\n",
-    "scipy.stats.probplot(ols_result.resid, plot=sns.mpl.pyplot)"
+    "_ = scipy.stats.probplot(ols_result.resid, plot=sns.mpl.pyplot)"
    ]
   },
   {
diff --git a/genindex.html b/genindex.html
index a6da118..6d587ec 100644
--- a/genindex.html
+++ b/genindex.html
@@ -95,7 +95,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="07-ResamplingAndSimulation.html">
-   Resampling and simulation in R
+   Resampling and simulation
   </a>
  </li>
  <li class="toctree-l1">
@@ -120,7 +120,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="13-GeneralLinearModel.html">
-   The General Linear Model in Python
+   The General Linear Model
   </a>
  </li>
 </ul>
diff --git a/index.html b/index.html
index 0b2b35b..76ff7fb 100644
--- a/index.html
+++ b/index.html
@@ -95,7 +95,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="07-ResamplingAndSimulation.html">
-   Resampling and simulation in R
+   Resampling and simulation
   </a>
  </li>
  <li class="toctree-l1">
@@ -120,7 +120,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="13-GeneralLinearModel.html">
-   The General Linear Model in Python
+   The General Linear Model
   </a>
  </li>
 </ul>
diff --git a/objects.inv b/objects.inv
index 85f90ae..db8c5f0 100644
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diff --git a/search.html b/search.html
index e35222b..9c121d0 100644
--- a/search.html
+++ b/search.html
@@ -99,7 +99,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="07-ResamplingAndSimulation.html">
-   Resampling and simulation in R
+   Resampling and simulation
   </a>
  </li>
  <li class="toctree-l1">
@@ -124,7 +124,7 @@ <h1 class="site-logo" id="site-title">Python Companion to Statistical Thinking i
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="13-GeneralLinearModel.html">
-   The General Linear Model in Python
+   The General Linear Model
   </a>
  </li>
 </ul>
diff --git a/searchindex.js b/searchindex.js
index 4ac1bd5..986bd5a 100644
--- a/searchindex.js
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