capitalize the reference in the menu

_quarto.yml

@@ -53,9 +53,7 @@ website:
         - text: "Effects Model"
           file: effects_model.qmd
         - text: "ANOVA and the F-test"
-          href: Anova_F-test.qmd
-        - text: "ANOVA and the F-test (alternate link)"
-          href: Anova_F-test.html
+          href: ANOVA_F-test.qmd
         - text: "Assumptions"
           href: assumptions.qmd
         - text: "Contrasts"

docs/ANOVA_F-test.html

@@ -209,12 +209,8 @@
  <span class="dropdown-text">Effects Model</span></a>
   </li>  
         <li>
-    <a class="dropdown-item" href="./Anova_F-test.html">
+    <a class="dropdown-item" href="./ANOVA_F-test.qmd">
  <span class="dropdown-text">ANOVA and the F-test</span></a>
-  </li>  
-        <li>
-    <a class="dropdown-item" href="./Anova_F-test.html">
- <span class="dropdown-text">ANOVA and the F-test (alternate link)</span></a>
   </li>  
         <li>
     <a class="dropdown-item" href="./assumptions.html">

docs/Anova_F-test.qmd

@@ -158,7 +158,7 @@ $$
 With this is mind, we can fill in the F column of the ANOVA table for Treatment Factor.
 
 | Source | df | SS | MS | F | p-value |
-|:--------|:-------:|:-------:|:-------:|:-----------------------------:|:-------:|
+|:---|:--:|:--:|:--:|:--:|:--:|
 | Grand Mean |  |  |  |  |  |
 | Treatment Factor |  |  |  | $\frac{MS_\text{Treatment Factor}}{MS_\text{Residual Error}}$ |  |
 | Residual Error |  |  |  |  |  |
@@ -280,7 +280,7 @@ Recall that an effect is defined as a deviation from the mean.
 Therefore, in a mean square calculation, the numerator is the sum of squares and the denominator is the degrees of freedom.
 
 | Source | df | SS | MS | F | p-value |
-|:--------|:-------:|:-------:|:-----------------:|:----------------:|:-------:|
+|:---|:--:|:--:|:--:|:--:|:--:|
 | Grand Mean |  |  |  |  |  |
 | Treatment Factor |  |  | $\frac{SS_\text{Treatment Factor}}{df_\text{Treatment Factor}}$ | $\frac{MS_\text{Treatment Factor}}{MS_\text{Residual Error}}$ |  |
 | Residual Error |  |  | $\frac{SS_\text{Residual Error}}{df_\text{Residual Error}}$ |  |  |
@@ -295,7 +295,7 @@ To avoid this problem, statisticians square the distances before summing them. T
 In the table below an equation for each factor's SS is listed using terms from the factor effects model. We'll walk through the meaning of each of those equations.
 
 | Source | df | SS | MS | F | p-value |
-|:----------|:---------:|:---------:|:--------------:|:-------------:|:---------:|
+|:---|:--:|:--:|:--:|:--:|:--:|
 | Grand Mean |  | $n*\bar{y}_\text{..}^2$ |  |  |  |
 | Treatment Factor |  | $$ \sum (\hat{\alpha}_i^2*n_i)$$ | $\frac{SS_\text{Treatment Factor}}{df_\text{Treatment Factor}}$ | $\frac{MS_\text{Treatment Factor}}{MS_\text{Residual Error}}$ |  |
 | Residual Error |  | $$ \sum \hat{\epsilon}_\text{ij}^2 $$ | $\frac{SS_\text{Residual Error}}{df_\text{Residual Error}}$ |  |  |
@@ -335,7 +335,7 @@ For additional explanation, consider this simple example. There are three data p
 The example described above is summarized in @tbl-df-example. The first number is represented as an $a$ and the second number if represented with a $b$.
 
 |   value 1    |   value 2    |           value 3           |     | Mean of 3 Values |
-|:-----------:|:-----------:|:-------------------:|:-----------:|:-----------:|
+|:------------:|:------------:|:---------------------------:|:---:|:----------------:|
 |      a       |      b       |       $3*10 - (a+b)$        | -\> |        10        |
 | free to vary | free to vary | depends on other two values |     |                  |