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10-MultipleRegression-FactorialDesigns.html

@@ -480,6 +480,18 @@
     <li>
       <a href="Practicum_1_Voorbereiding_3.html">Practicum 1: Voorbereiding 3</a>
     </li>
+    <li>
+      <a href="Practicum_1_oplossing_Two_sample_t_test.html">Practicum 1: Oplossing Voorbereiding 1</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_anova.html">Practicum 1: Oplossing Voorbereiding 2</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_niet_parametrische_test.html">Practicum 1: Oplossing Voorbereiding 3</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oefening_4.html">Practicum 2: Oefening lineair model</a>
+    </li>
   </ul>
 </li>
       </ul>
@@ -725,15 +737,15 @@ <h2><span class="header-section-number">4.1</span> Verwijderen van
 
 Linear Hypotheses:
                       Estimate Std. Error t value Pr(&gt;|t|)    
-poison: II - I == 0    0.04686    0.01744   2.688  0.07357 .  
+poison: II - I == 0    0.04686    0.01744   2.688  0.07349 .  
 poison: III - I == 0   0.19964    0.01744  11.451  &lt; 0.001 ***
 poison: III - II == 0  0.15278    0.01744   8.763  &lt; 0.001 ***
 treat: B - A == 0     -0.16574    0.02013  -8.233  &lt; 0.001 ***
-treat: C - A == 0     -0.05721    0.02013  -2.842  0.05098 .  
+treat: C - A == 0     -0.05721    0.02013  -2.842  0.05093 .  
 treat: D - A == 0     -0.13583    0.02013  -6.747  &lt; 0.001 ***
 treat: C - B == 0      0.10853    0.02013   5.391  &lt; 0.001 ***
 treat: D - B == 0      0.02991    0.02013   1.485  0.61546    
-treat: D - C == 0     -0.07862    0.02013  -3.905  0.00278 ** 
+treat: D - C == 0     -0.07862    0.02013  -3.905  0.00288 ** 
 ---
 Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
 (Adjusted p values reported -- single-step method)</code></pre>
@@ -752,15 +764,15 @@ <h2><span class="header-section-number">4.1</span> Verwijderen van
 
 Linear Hypotheses:
                       Estimate   lwr        upr       
-poison: II - I == 0    0.0468641 -0.0027970  0.0965253
-poison: III - I == 0   0.1996425  0.1499814  0.2493036
-poison: III - II == 0  0.1527784  0.1031172  0.2024395
-treat: B - A == 0     -0.1657402 -0.2230840 -0.1083965
-treat: C - A == 0     -0.0572135 -0.1145573  0.0001302
-treat: D - A == 0     -0.1358338 -0.1931776 -0.0784901
-treat: C - B == 0      0.1085267  0.0511830  0.1658704
-treat: D - B == 0      0.0299064 -0.0274373  0.0872502
-treat: D - C == 0     -0.0786203 -0.1359640 -0.0212765</code></pre>
+poison: II - I == 0    0.0468641 -0.0027958  0.0965241
+poison: III - I == 0   0.1996425  0.1499826  0.2493024
+poison: III - II == 0  0.1527784  0.1031184  0.2024383
+treat: B - A == 0     -0.1657402 -0.2230826 -0.1083979
+treat: C - A == 0     -0.0572135 -0.1145559  0.0001288
+treat: D - A == 0     -0.1358338 -0.1931762 -0.0784915
+treat: C - B == 0      0.1085267  0.0511844  0.1658690
+treat: D - B == 0      0.0299064 -0.0274359  0.0872488
+treat: D - C == 0     -0.0786203 -0.1359626 -0.0212779</code></pre>
 <div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(comparisons,<span class="at">yaxt=</span><span class="st">&quot;none&quot;</span>)</span>
 <span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>contrastNames <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;II-I&quot;</span>,<span class="st">&quot;III-I&quot;</span>,<span class="st">&quot;III-II&quot;</span>,<span class="st">&quot;B-A&quot;</span>,<span class="st">&quot;C-A&quot;</span>,<span class="st">&quot;D-A&quot;</span>,<span class="st">&quot;C-B&quot;</span>,<span class="st">&quot;D-B&quot;</span>,<span class="st">&quot;D-C&quot;</span>)</span>
 <span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a><span class="fu">axis</span>(<span class="dv">2</span>,<span class="at">at=</span><span class="fu">c</span>(<span class="fu">length</span>(contrastNames)<span class="sc">:</span><span class="dv">1</span>), <span class="at">labels=</span>contrastNames,<span class="at">las=</span><span class="dv">2</span>)</span></code></pre></div>

10-MultipleRegression.html

@@ -480,6 +480,18 @@
     <li>
       <a href="Practicum_1_Voorbereiding_3.html">Practicum 1: Voorbereiding 3</a>
     </li>
+    <li>
+      <a href="Practicum_1_oplossing_Two_sample_t_test.html">Practicum 1: Oplossing Voorbereiding 1</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_anova.html">Practicum 1: Oplossing Voorbereiding 2</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_niet_parametrische_test.html">Practicum 1: Oplossing Voorbereiding 3</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oefening_4.html">Practicum 2: Oefening lineair model</a>
+    </li>
   </ul>
 </li>
       </ul>

10-Recap-AlgemeenLineairModel-AdditiefModel.html

@@ -480,6 +480,18 @@
     <li>
       <a href="Practicum_1_Voorbereiding_3.html">Practicum 1: Voorbereiding 3</a>
     </li>
+    <li>
+      <a href="Practicum_1_oplossing_Two_sample_t_test.html">Practicum 1: Oplossing Voorbereiding 1</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_anova.html">Practicum 1: Oplossing Voorbereiding 2</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_niet_parametrische_test.html">Practicum 1: Oplossing Voorbereiding 3</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oefening_4.html">Practicum 2: Oefening lineair model</a>
+    </li>
   </ul>
 </li>
       </ul>

10-Recap-AlgemeenLineairModel-OverigeConcepten.html

@@ -480,6 +480,18 @@
     <li>
       <a href="Practicum_1_Voorbereiding_3.html">Practicum 1: Voorbereiding 3</a>
     </li>
+    <li>
+      <a href="Practicum_1_oplossing_Two_sample_t_test.html">Practicum 1: Oplossing Voorbereiding 1</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_anova.html">Practicum 1: Oplossing Voorbereiding 2</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_niet_parametrische_test.html">Practicum 1: Oplossing Voorbereiding 3</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oefening_4.html">Practicum 2: Oefening lineair model</a>
+    </li>
   </ul>
 </li>
       </ul>
@@ -917,9 +929,9 @@ <h3><span class="header-section-number">4.1.2</span> Besluitvorming</h3>
 ## 
 ## Linear Hypotheses:
 ##                           Estimate Std. Error t value Pr(&gt;|t|)    
-## Age == 0                   0.43999    0.01511  29.127   &lt;1e-10 ***
-## Age + Age:Gendermale == 0  0.24840    0.01879  13.217   &lt;1e-10 ***
-## Age:Gendermale == 0       -0.19159    0.02411  -7.946   &lt;1e-10 ***
+## Age == 0                   0.43999    0.01511  29.127  &lt; 2e-16 ***
+## Age + Age:Gendermale == 0  0.24840    0.01879  13.217  &lt; 2e-16 ***
+## Age:Gendermale == 0       -0.19159    0.02411  -7.946 2.66e-15 ***
 ## ---
 ## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
 ## (Adjusted p values reported -- single-step method)</code></pre>
@@ -930,14 +942,14 @@ <h3><span class="header-section-number">4.1.2</span> Besluitvorming</h3>
 ## 
 ## Fit: lm(formula = BPSysAve ~ Age * Gender, data = bpData, weights = 1/mSd$fitted^2)
 ## 
-## Quantile = 2.3168
+## Quantile = 2.3171
 ## 95% family-wise confidence level
 ##  
 ## 
 ## Linear Hypotheses:
 ##                           Estimate lwr     upr    
 ## Age == 0                   0.4400   0.4050  0.4750
-## Age + Age:Gendermale == 0  0.2484   0.2049  0.2919
+## Age + Age:Gendermale == 0  0.2484   0.2048  0.2919
 ## Age:Gendermale == 0       -0.1916  -0.2475 -0.1357</code></pre>
 </div>
 <div id="conclusie" class="section level3" number="4.1.3">

Examenvoorbeeld_oefening_1.html

@@ -480,6 +480,18 @@
     <li>
       <a href="Practicum_1_Voorbereiding_3.html">Practicum 1: Voorbereiding 3</a>
     </li>
+    <li>
+      <a href="Practicum_1_oplossing_Two_sample_t_test.html">Practicum 1: Oplossing Voorbereiding 1</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_anova.html">Practicum 1: Oplossing Voorbereiding 2</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_niet_parametrische_test.html">Practicum 1: Oplossing Voorbereiding 3</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oefening_4.html">Practicum 2: Oefening lineair model</a>
+    </li>
   </ul>
 </li>
       </ul>
@@ -768,7 +780,7 @@ <h1><span class="header-section-number">3</span> Hypotheses testen</h1>
 ##                            Estimate Std. Error t value Pr(&gt;|t|)    
 ## bacon == 0                 -1.19081    0.13182  -9.034   &lt;0.001 ***
 ## bacon + bacon:sexmale == 0 -0.87572    0.06286 -13.930   &lt;0.001 ***
-## bacon:sexmale == 0          0.31509    0.14603   2.158   0.0735 .  
+## bacon:sexmale == 0          0.31509    0.14603   2.158   0.0734 .  
 ## ---
 ## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
 ## (Adjusted p values reported -- single-step method)</code></pre>
@@ -779,15 +791,15 @@ <h1><span class="header-section-number">3</span> Hypotheses testen</h1>
 ## 
 ## Fit: lm(formula = permeat ~ bacon + length + sex + bacon:sex, data = pigs)
 ## 
-## Quantile = 2.318
+## Quantile = 2.3202
 ## 95% family-wise confidence level
 ##  
 ## 
 ## Linear Hypotheses:
-##                            Estimate lwr     upr    
-## bacon == 0                 -1.1908  -1.4964 -0.8852
-## bacon + bacon:sexmale == 0 -0.8757  -1.0214 -0.7300
-## bacon:sexmale == 0          0.3151  -0.0234  0.6536</code></pre>
+##                            Estimate lwr      upr     
+## bacon == 0                 -1.19081 -1.49667 -0.88496
+## bacon + bacon:sexmale == 0 -0.87572 -1.02158 -0.72986
+## bacon:sexmale == 0          0.31509 -0.02373  0.65391</code></pre>
 </div>
 <div id="conclusie" class="section level1" number="4">
 <h1><span class="header-section-number">4</span> Conclusie</h1>
@@ -798,7 +810,7 @@ <h1><span class="header-section-number">4</span> Conclusie</h1>
 (p&lt;&lt;0.001) als mannelijke varkens (p&lt;0.001). Voor zeugen, die
 een verschillende dikte van speklaag hebben, is het vleespercentage
 gemiddeld 1.19 %/mm lager bij zeugen met de dikste speklaag (95% BI
-[0.89 %/mm, 1.5 %/mm]). Voor beren (mannelijke varkens), die een
+[0.88 %/mm, 1.5 %/mm]). Voor beren (mannelijke varkens), die een
 verschillende dikte van speklaag hebben, is het vleespercentage
 gemiddeld 0.88 %/mm lager bij beren met de dikste speklaag (95% BI [0.73
 %/mm, 1.02 %/mm]). Er is op het 5% significantieniveau geen significant

Examenvoorbeeld_oefening_1_opgave.html

@@ -390,6 +390,18 @@
     <li>
       <a href="Practicum_1_Voorbereiding_3.html">Practicum 1: Voorbereiding 3</a>
     </li>
+    <li>
+      <a href="Practicum_1_oplossing_Two_sample_t_test.html">Practicum 1: Oplossing Voorbereiding 1</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_anova.html">Practicum 1: Oplossing Voorbereiding 2</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_niet_parametrische_test.html">Practicum 1: Oplossing Voorbereiding 3</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oefening_4.html">Practicum 2: Oefening lineair model</a>
+    </li>
   </ul>
 </li>
       </ul>

Examenvoorbeeld_oefening_2.html

@@ -480,6 +480,18 @@
     <li>
       <a href="Practicum_1_Voorbereiding_3.html">Practicum 1: Voorbereiding 3</a>
     </li>
+    <li>
+      <a href="Practicum_1_oplossing_Two_sample_t_test.html">Practicum 1: Oplossing Voorbereiding 1</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_anova.html">Practicum 1: Oplossing Voorbereiding 2</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_niet_parametrische_test.html">Practicum 1: Oplossing Voorbereiding 3</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oefening_4.html">Practicum 2: Oefening lineair model</a>
+    </li>
   </ul>
 </li>
       </ul>
@@ -728,24 +740,24 @@ <h1><span class="header-section-number">3</span> Hypotheses testen</h1>
 ## Fit: lm(formula = intensity %&gt;% log2 ~ location * tissue + patient, 
 ##     data = hart)
 ## 
-## Quantile = 3.0268
+## Quantile = 3.0293
 ## 95% family-wise confidence level
 ##  
 ## 
 ## Linear Hypotheses:
 ##                                  Estimate lwr     upr    
-## tissueV == 0                      7.9680   6.5961  9.3398
-## tissueV + locationR:tissueV == 0  5.5038   4.1320  6.8757
-## locationR:tissueV == 0           -2.4641  -4.4042 -0.5240</code></pre>
+## tissueV == 0                      7.9680   6.5950  9.3409
+## tissueV + locationR:tissueV == 0  5.5038   4.1308  6.8768
+## locationR:tissueV == 0           -2.4641  -4.4058 -0.5224</code></pre>
 <div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span><span class="sc">^</span><span class="fu">confint</span>(mcp)<span class="sc">$</span>confint</span></code></pre></div>
 <pre><code>##                                Estimate         lwr         upr
-## tissueV                     250.3757829 96.94415082 646.6406911
-## tissueV + locationR:tissueV  45.3753769 17.56910085 117.1901081
-## locationR:tissueV             0.1812291  0.04736657   0.6934001
+## tissueV                     250.3757829 96.89264124 646.9844549
+## tissueV + locationR:tissueV  45.3753769 17.55976581 117.2524081
+## locationR:tissueV             0.1812291  0.04733098   0.6939215
 ## attr(,&quot;conf.level&quot;)
 ## [1] 0.95
 ## attr(,&quot;calpha&quot;)
-## [1] 3.020156</code></pre>
+## [1] 3.021848</code></pre>
 </div>
 <div id="conclusie" class="section level1" number="4">
 <h1><span class="header-section-number">4</span> Conclusie:</h1>
@@ -754,10 +766,10 @@ <h1><span class="header-section-number">4</span> Conclusie:</h1>
 0.001).</p>
 <p>Het geometrisch gemiddelde van de expressie van Myosin3L is een
 factor 250 hoger in het linkerventrikel dan in het linkeratrium (p &lt;
-0.001, 95% BI [97.1, 645.8]).</p>
+0.001, 95% BI [96.8, 647.4]).</p>
 <p>Het geometrisch gemiddelde van de expressie van Myosin3L is een
 factor 45 hoger in het rechterventrikel dan in het rechteratrium (p &lt;
-0.001, 95% BI [17.6, 117.3]).</p>
+0.001, 95% BI [17.5, 117.4]).</p>
 <p>De opregulatie van Myosin3L in ventrikel vs het atrium is gemiddeld
 een factor 6 hoger in de linker- dan in de rechterzijde (p = 0.019, 95%
 BI [1.4, 21.1] ).</p>

Examenvoorbeeld_oefening_2_opgave.html

@@ -480,6 +480,18 @@
     <li>
       <a href="Practicum_1_Voorbereiding_3.html">Practicum 1: Voorbereiding 3</a>
     </li>
+    <li>
+      <a href="Practicum_1_oplossing_Two_sample_t_test.html">Practicum 1: Oplossing Voorbereiding 1</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_anova.html">Practicum 1: Oplossing Voorbereiding 2</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_niet_parametrische_test.html">Practicum 1: Oplossing Voorbereiding 3</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oefening_4.html">Practicum 2: Oefening lineair model</a>
+    </li>
   </ul>
 </li>
       </ul>

Exp_design_fast_1_ex.html

@@ -480,6 +480,18 @@
     <li>
       <a href="Practicum_1_Voorbereiding_3.html">Practicum 1: Voorbereiding 3</a>
     </li>
+    <li>
+      <a href="Practicum_1_oplossing_Two_sample_t_test.html">Practicum 1: Oplossing Voorbereiding 1</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_anova.html">Practicum 1: Oplossing Voorbereiding 2</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_niet_parametrische_test.html">Practicum 1: Oplossing Voorbereiding 3</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oefening_4.html">Practicum 2: Oefening lineair model</a>
+    </li>
   </ul>
 </li>
       </ul>

Exp_design_fast_1_sol.html

@@ -480,6 +480,18 @@
     <li>
       <a href="Practicum_1_Voorbereiding_3.html">Practicum 1: Voorbereiding 3</a>
     </li>
+    <li>
+      <a href="Practicum_1_oplossing_Two_sample_t_test.html">Practicum 1: Oplossing Voorbereiding 1</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_anova.html">Practicum 1: Oplossing Voorbereiding 2</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oplossing_niet_parametrische_test.html">Practicum 1: Oplossing Voorbereiding 3</a>
+    </li>
+    <li>
+      <a href="Practicum_1_oefening_4.html">Practicum 2: Oefening lineair model</a>
+    </li>
   </ul>
 </li>
       </ul>
@@ -702,9 +714,9 @@ <h2><span class="header-section-number">4.2</span> Simulation</h2>
 <span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a>alpha <span class="ot">&lt;-</span> <span class="fl">0.05</span> </span>
 <span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a>power <span class="ot">&lt;-</span> <span class="fu">simFast</span>(form, rodents, betas, sd, <span class="at">contrasts =</span> contrast, <span class="at">alpha =</span> alpha, <span class="at">nSim =</span> nSim)</span>
 <span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a>power</span></code></pre></div>
-<pre><code>## [1] 0.2213</code></pre>
+<pre><code>## [1] 0.2248</code></pre>
 <p>We observe that the experiment is severly underpowered. We only have
-a power of 22.1% to pick up the treatment effect.</p>
+a power of 22.5% to pick up the treatment effect.</p>
 </div>
 </div>
 <div id="power-for-a-balanced-design" class="section level1" number="5">
@@ -721,7 +733,7 @@ <h1><span class="header-section-number">5</span> Power for a balanced
 <span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>predictorData <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">group =</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="st">&quot;ctrl&quot;</span>,<span class="st">&quot;treat&quot;</span>),<span class="fu">c</span>(n1,n2)) <span class="sc">%&gt;%</span> as.factor)</span>
 <span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a>powerBalanced <span class="ot">&lt;-</span> <span class="fu">simFast</span>(form, predictorData, betas, sd, <span class="at">contrasts =</span> contrast, <span class="at">alpha =</span> alpha, <span class="at">nSim =</span> nSim)</span>
 <span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a>powerBalanced</span></code></pre></div>
-<pre><code>## [1] 0.2406</code></pre>
+<pre><code>## [1] 0.2444</code></pre>
 <p>We observe that the power is larger for the balanced design. We could
 also have known this from formula of the standard error from the
 two-sample t-test.</p>

Exp_design_fast_2_ex.html

@@ -480,6 +480,18 @@
     <li>
       <a href="Practicum_1_Voorbereiding_3.html">Practicum 1: Voorbereiding 3</a>
     </li>
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