diff --git a/module4/challenger.html b/module4/challenger.html
index af5b8c47ce3d9b80f06b5cb0f70c4dbd47d0cfe0..2cfa7faf4444873420e377efe0834d2d5b6504e3 100644
--- a/module4/challenger.html
+++ b/module4/challenger.html
@@ -773,9 +773,9 @@ Status `~/tmp/MOOC-RR/module4/Project.toml`
 
 
 
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     <script>
-    PLOT = document.getElementById('8196e83b-fcfd-404d-a407-99bab186c19a');
+    PLOT = document.getElementById('7db6b95a-3f30-4f91-8d9e-d26a8f7a5f59');
     Plotly.plot(PLOT, [
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@@ -1003,7 +1003,7 @@ Status &#96;~/tmp/MOOC-RR/module4/Project.toml&#96;
 
 
 <pre class="output">
-StatsModels.TableRegressionModel&#123;GeneralizedLinearModel&#123;GLM.GlmResp&#123;Array&#123;Float64,1&#125;,Binomial&#123;Float64&#125;,LogitLink&#125;,GLM.DensePredChol&#123;Float64,LinearAlgebra.Cholesky&#123;Float64,Array&#123;Float64,2&#125;&#125;&#125;&#125;,Array&#123;Float64,2&#125;&#125;
+StatsModels.TableRegressionModel&#123;GLM.GeneralizedLinearModel&#123;GLM.GlmResp&#123;Array&#123;Float64,1&#125;,Distributions.Binomial&#123;Float64&#125;,GLM.LogitLink&#125;,GLM.DensePredChol&#123;Float64,LinearAlgebra.Cholesky&#123;Float64,Array&#123;Float64,2&#125;&#125;&#125;&#125;,Array&#123;Float64,2&#125;&#125;
 
 Frequency ~ 1 &#43; Temperature
 
@@ -1018,22 +1018,15 @@ Temperature  -0.115601    0.115184  -1.00362    0.3156  -0.341358   0.110156
 
 
 <p>The maximum likelyhood estimator of the intercept and of Temperature are thus <span class="math">$\hat{\alpha}=5.085$</span> and <span class="math">$\hat{\beta}=-0.116$</span>.</p>
-<p>This corresponds to the values from the article of Dalal et al. The standard errors are <span class="math">$s_{\hat{\alpha}} = 7.477$</span> and <span class="math">$s_{\hat{\beta}} = 0.115$</span>, which is different from the <span class="math">$3.052$</span> and <span class="math">$0.047$</span> reported by Dallal et al. The deviance is <span class="math">$G^2 = 3.014$</span> with 22 degrees of freedom.</p>
-<p>I cannot find any value similar to the Goodness of fit &#40;<span class="math">$G^2=18.086$</span>&#41; reported by Dalal et al. However, the number of degrees of freedom is similar to theirs &#40;21&#41;.</p>
-<p>There seems to be something wrong. Oh I know, I haven&#39;t indicated that my observations are actually the result of 6 observations for each rocket launch. The correct way to do this would be to weight the data using the <code>Count</code> column. Since I don&#39;t know how to do that with the <a href="https://github.com/JuliaStats/GLM.jl">GLM</a> package I&#39;m using, I will simply duplicate the data:</p>
+<p>This corresponds to the values from the article of Dalal et al. The standard errors are <span class="math">$s_{\hat{\alpha}} = 7.477$</span> and <span class="math">$s_{\hat{\beta}} = 0.115$</span>, which is different from the <span class="math">$3.052$</span> and <span class="math">$0.047$</span> reported by Dallal et al.</p>
+<p>The deviance is <span class="math">$G^2 = 3.014$</span> with 22 degrees of freedom. I cannot find any value similar to the Goodness of fitreported by Dalal <em>et al.</em> &#40;<span class="math">$G^2=18.086$</span>&#41;. However, the number of degrees of freedom is different but at least similar to theirs &#40;21&#41;.</p>
+<p>There seems to be something wrong. Oh I know, I haven&#39;t indicated that my observations are actually the result of 6 observations for each rocket launch. Let&#39;s indicate these weights &#40;since the weights are always the same throughout all experiments, it does not change the estimates of the fit but it does influence de variance estimate&#41;.</p>
 
 
 <pre class='hljl'>
-<span class='hljl-n'>weighted_data</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nf'>DataFrame</span><span class='hljl-p'>(</span><span class='hljl-n'>Temperature</span><span class='hljl-oB'>=</span><span class='hljl-n'>Int</span><span class='hljl-p'>[],</span><span class='hljl-t'> </span><span class='hljl-n'>Frequency</span><span class='hljl-oB'>=</span><span class='hljl-n'>Float64</span><span class='hljl-p'>[])</span><span class='hljl-t'>
-</span><span class='hljl-k'>for</span><span class='hljl-t'> </span><span class='hljl-n'>row</span><span class='hljl-t'> </span><span class='hljl-kp'>in</span><span class='hljl-t'> </span><span class='hljl-nf'>eachrow</span><span class='hljl-p'>(</span><span class='hljl-n'>data</span><span class='hljl-p'>)</span><span class='hljl-t'>
-    </span><span class='hljl-k'>for</span><span class='hljl-t'> </span><span class='hljl-n'>_</span><span class='hljl-t'> </span><span class='hljl-kp'>in</span><span class='hljl-t'> </span><span class='hljl-ni'>1</span><span class='hljl-oB'>:</span><span class='hljl-n'>row</span><span class='hljl-oB'>.</span><span class='hljl-n'>Count</span><span class='hljl-t'>
-        </span><span class='hljl-nf'>push!</span><span class='hljl-p'>(</span><span class='hljl-n'>weighted_data</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-p'>(</span><span class='hljl-n'>Temperature</span><span class='hljl-oB'>=</span><span class='hljl-n'>row</span><span class='hljl-oB'>.</span><span class='hljl-n'>Temperature</span><span class='hljl-p'>,</span><span class='hljl-t'>
-                              </span><span class='hljl-n'>Frequency</span><span class='hljl-oB'>=</span><span class='hljl-n'>row</span><span class='hljl-oB'>.</span><span class='hljl-n'>Frequency</span><span class='hljl-p'>))</span><span class='hljl-t'>
-    </span><span class='hljl-k'>end</span><span class='hljl-t'>
-</span><span class='hljl-k'>end</span><span class='hljl-t'>
-
-</span><span class='hljl-n'>model</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nf'>glm</span><span class='hljl-p'>(</span><span class='hljl-nd'>@formula</span><span class='hljl-p'>(</span><span class='hljl-n'>Frequency</span><span class='hljl-t'> </span><span class='hljl-oB'>~</span><span class='hljl-t'> </span><span class='hljl-n'>Temperature</span><span class='hljl-p'>),</span><span class='hljl-t'> </span><span class='hljl-n'>weighted_data</span><span class='hljl-p'>,</span><span class='hljl-t'>
-            </span><span class='hljl-nf'>Binomial</span><span class='hljl-p'>(),</span><span class='hljl-t'> </span><span class='hljl-nf'>LogitLink</span><span class='hljl-p'>())</span><span class='hljl-t'>
+<span class='hljl-n'>model</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nf'>glm</span><span class='hljl-p'>(</span><span class='hljl-nd'>@formula</span><span class='hljl-p'>(</span><span class='hljl-n'>Frequency</span><span class='hljl-t'> </span><span class='hljl-oB'>~</span><span class='hljl-t'> </span><span class='hljl-n'>Temperature</span><span class='hljl-p'>),</span><span class='hljl-t'> </span><span class='hljl-n'>data</span><span class='hljl-p'>,</span><span class='hljl-t'>
+            </span><span class='hljl-nf'>Binomial</span><span class='hljl-p'>(),</span><span class='hljl-t'> </span><span class='hljl-nf'>LogitLink</span><span class='hljl-p'>();</span><span class='hljl-t'>
+            </span><span class='hljl-n'>wts</span><span class='hljl-oB'>=</span><span class='hljl-n'>data</span><span class='hljl-oB'>.</span><span class='hljl-n'>Count</span><span class='hljl-p'>)</span><span class='hljl-t'>
 
 </span><span class='hljl-n'>α</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>β</span><span class='hljl-t'>   </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nf'>coef</span><span class='hljl-p'>(</span><span class='hljl-n'>model</span><span class='hljl-p'>)</span><span class='hljl-t'>
 </span><span class='hljl-n'>σα</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>σβ</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nf'>stderror</span><span class='hljl-p'>(</span><span class='hljl-n'>model</span><span class='hljl-p'>)</span><span class='hljl-t'>
@@ -1046,7 +1039,7 @@ Temperature  -0.115601    0.115184  -1.00362    0.3156  -0.341358   0.110156
 
 
 <pre class="output">
-StatsModels.TableRegressionModel&#123;GeneralizedLinearModel&#123;GLM.GlmResp&#123;Array&#123;Float64,1&#125;,Binomial&#123;Float64&#125;,LogitLink&#125;,GLM.DensePredChol&#123;Float64,LinearAlgebra.Cholesky&#123;Float64,Array&#123;Float64,2&#125;&#125;&#125;&#125;,Array&#123;Float64,2&#125;&#125;
+StatsModels.TableRegressionModel&#123;GLM.GeneralizedLinearModel&#123;GLM.GlmResp&#123;Array&#123;Float64,1&#125;,Distributions.Binomial&#123;Float64&#125;,GLM.LogitLink&#125;,GLM.DensePredChol&#123;Float64,LinearAlgebra.Cholesky&#123;Float64,Array&#123;Float64,2&#125;&#125;&#125;&#125;,Array&#123;Float64,2&#125;&#125;
 
 Frequency ~ 1 &#43; Temperature
 
@@ -1054,14 +1047,14 @@ Coefficients:
 ─────────────────────────────────────────────────────────────────────────────
               Estimate  Std. Error   z value  Pr&#40;&gt;|z|&#41;  Lower 95&#37;   Upper 95&#37;
 ─────────────────────────────────────────────────────────────────────────────
-&#40;Intercept&#41;   5.08498    3.05248     1.66585    0.0957  -0.897782  11.0677
-Temperature  -0.115601   0.0470238  -2.45835    0.0140  -0.207766  -0.0234362
+&#40;Intercept&#41;   5.08498    3.05247     1.66585    0.0957  -0.897762  11.0677
+Temperature  -0.115601   0.0470236  -2.45836    0.0140  -0.207766  -0.0234366
 ─────────────────────────────────────────────────────────────────────────────
 </pre>
 
 
 <p>Good, now I have recovered the asymptotic standard errors <span class="math">$s_{\hat{\alpha}} = 3.052$</span> and <span class="math">$s_{\hat{\beta}} = 0.047$</span>,</p>
-<p>The Goodness of fit &#40;Deviance&#41; indicated for this model is <span class="math">$G^2 = 18.086$</span> with 137 degrees of freedom. Now <span class="math">$G^2$</span> is in good accordance to the results of the Dalal <em>et al.</em> article, but the number of degrees of freedom is 6 times larger than i should, due to my tampering of the data to duplicate them instead of weighting them.</p>
+<p>The Goodness of fit &#40;Deviance&#41; indicated for this model is <span class="math">$G^2 = 18.086$</span> with 137 degrees of freedom. Now <span class="math">$G^2$</span> is in good accordance to the results of the Dalal <em>et al.</em> article, but the number of degrees of freedom is approximately 6 times larger than that of Dalal <em>et al</em>. Note that, even removing this factor &#40;which is probably due to the way the number of residual degrees of freedom are defined in both libraries in the presence of weights&#41;, the values are similar but still differ by  9&#37;.</p>
 <h1>Predicting failure probability</h1>
 <p>The temperature when launching the shuttle was 31°F. Let&#39;s try to estimate the failure probability for such temperature using our model:</p>
 
@@ -1078,9 +1071,9 @@ Temperature  -0.115601   0.0470238  -2.45835    0.0140  -0.207766  -0.0234362
 
 
 
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@@ -1417,247 +1410,247 @@ Temperature  -0.115601   0.0470238  -2.45835    0.0140  -0.207766  -0.0234362
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         ],
         "type": "scatter"
     }
@@ -1742,8 +1735,8 @@ Temperature  -0.115601   0.0470238  -2.45835    0.0140  -0.207766  -0.0234362
         "visible": true,
         "ticks": "inside",
         "range": [
-            -0.02503118983596447,
-            0.8594041843681135
+            -0.02503118983466198,
+            0.8594041843233947
         ],
         "domain": [
             0.07897368948673088,
diff --git a/module4/challenger.jmd b/module4/challenger.jmd
index 75f7f326788e3fe345f917d1c8850d96e55ab903..8df9070a86e983af55888d03c521c4e23191f16e 100644
--- a/module4/challenger.jmd
+++ b/module4/challenger.jmd
@@ -99,32 +99,23 @@ This corresponds to the values from the article of Dalal et al. The standard
 errors are
 $s_{\hat{\alpha}} = `j @printf "%.3f" σα`$ and
 $s_{\hat{\beta}} = `j @printf "%.3f" σβ`$,
-which is different from the $3.052$ and $0.047$ reported by Dallal et al. The
-deviance is
-$G^2 = `j @printf "%.3f" G²`$ with `j nDOF` degrees of freedom.
+which is different from the $3.052$ and $0.047$ reported by Dallal et al.
 
-I cannot find any value similar to the Goodness of fit ($G^2=18.086$) reported
-by Dalal et al. However, the number of degrees of freedom is similar to theirs
-(21).
+The deviance is $G^2 = `j @printf "%.3f" G²`$ with `j nDOF` degrees of freedom.
+I cannot find any value similar to the Goodness of fitreported by Dalal *et al.*
+($G^2=18.086$). However, the number of degrees of freedom is different but at
+least similar to theirs (21).
 
 There seems to be something wrong. Oh I know, I haven't indicated that my
 observations are actually the result of 6 observations for each rocket
-launch. The correct way to do this would be to weight the data using the `Count`
-column. Since I don't know how to do that with the
-[GLM](https://github.com/JuliaStats/GLM.jl) package I'm using, I will simply
-duplicate the data:
+launch. Let's indicate these weights (since the weights are always the same
+throughout all experiments, it does not change the estimates of the fit but it
+does influence de variance estimate).
 
 ```julia; wrap=false; hold=true
-weighted_data = DataFrame(Temperature=Int[], Frequency=Float64[])
-for row in eachrow(data)
-    for _ in 1:row.Count
-        push!(weighted_data, (Temperature=row.Temperature,
-                              Frequency=row.Frequency))
-    end
-end
-
-model = glm(@formula(Frequency ~ Temperature), weighted_data,
-            Binomial(), LogitLink())
+model = glm(@formula(Frequency ~ Temperature), data,
+            Binomial(), LogitLink();
+            wts=data.Count)
 
 α, β   = coef(model)
 σα, σβ = stderror(model)
@@ -142,8 +133,11 @@ $s_{\hat{\beta}} = `j @printf "%.3f" σβ`$,
 The Goodness of fit (Deviance) indicated for this model is
 $G^2 = `j @printf "%.3f" G²`$ with `j nDOF` degrees of freedom. Now $G^2$ is in
 good accordance to the results of the Dalal *et al.* article, but the number of
-degrees of freedom is 6 times larger than i should, due to my tampering of the
-data to duplicate them instead of weighting them.
+degrees of freedom is approximately 6 times larger than that of Dalal *et
+al*. Note that, even removing this factor (which is probably due to the way the
+number of residual degrees of freedom are defined in both libraries in the
+presence of weights), the values are similar but still differ by
+`j @printf "%2.0f" 100 * (nDOF/6/21 - 1)`%.
 
 # Predicting failure probability
 
diff --git a/module4/challenger.pdf b/module4/challenger.pdf
index 595c455951fba288f6c0661c75424e484c724ab8..9b9ba438fcf6c8f6f1631033ab57030922a546d1 100644
Binary files a/module4/challenger.pdf and b/module4/challenger.pdf differ