Edits

00-preface.Rmd

@@ -3,7 +3,7 @@
 \vspace{0.1in}
 
 <center>
-`r include_image("images/logos/Rlogo.png", html_opts = "height=100px", latex_opts = "height=15%")` \hfill &emsp; &emsp; &emsp; `r include_image("images/logos/RStudio-Logo-Blue-Gradient.png", html_opts = "height=100px", latex_opts = "height=15%")`
+`r include_image("images/logos/Rlogo.png", html_opts = "height=100px", latex_opts = "height=12%")` \hfill &emsp; &emsp; &emsp; `r include_image("images/logos/RStudio-Logo-Blue-Gradient.png", html_opts = "height=100px", latex_opts = "height=12%")`
 </center>
 
 **Help! I'm completely new to coding and I need to learn R and RStudio! What do I do?** 

09-hypothesis-testing.Rmd

@@ -358,7 +358,7 @@ Let's summarize the steps for hypothesis testing:
 4. Determine whether the result of the test is statistically significant (if the null is rejected) or non-significant (the null is not rejected).
 
 
-#### The simulation-based approach
+#### The simulation-based approach {-}
 
 When using a simulation-based approach such as the bootstrap percentile method, 
 we repeat the first two steps of the theory-based approach:
@@ -504,7 +504,7 @@ n_songs_genre <- n_songs / 2
 
 To begin the analysis, `r n_songs` tracks were selected at random from Spotify's song archive. We will use "song" and "track" interchangeably going forward. There were `r n_songs_genre` metal tracks and `r n_songs_genre` deep house tracks selected. 
 
-The `moderndive` package contains the data on the songs by genre in the `spotify_by_genre` data frame. There are six genres selected in that data (`country`, `deep-house`, `dubstep`, `hip-hop`, `metal`, and `rock`). You will have the opportunity to explore relationships with the other genres and popularity in the Learning checks. Let's explore this data by focusing on just `metal` and `deep-house` by looking at 12 randomly selected rows and our columns of interest in Table \@ref(tab:twelve-spotify). Note that we also group our selection so that each of the four possible groupings of `track_genre` and `popular_or_not` are selected.
+The `moderndive` package contains the data on the songs by genre in the `spotify_by_genre` data frame. There are six genres selected in that data (`country`, `deep-house`, `dubstep`, `hip-hop`, `metal`, and `rock`). You will have the opportunity to explore relationships with the other genres and popularity in the *Learning checks*. Let's explore this data by focusing on just `metal` and `deep-house` by looking at 12 randomly selected rows and our columns of interest in Table \@ref(tab:twelve-spotify). Note that we also group our selection so that each of the four possible groupings of `track_genre` and `popular_or_not` are selected.
 
 ```{r echo=FALSE}
 set.seed(2)

10-inference-for-regression.Rmd

@@ -2433,7 +2433,7 @@ if (!file.exists("images/boot_mlr_viz.png")) {
 }
 ```
 
-```{r boot-distn-slopes, echo=FALSE, out.width="70%", fig.height=12, fig.cap="Bootstrap distributions of partial slopes."}
+```{r boot-distn-slopes, echo=FALSE, out.width="68%", fig.height=12, fig.cap="Bootstrap distributions of partial slopes."}
 if(is_latex_output()) {
   knitr::include_graphics("images/boot_mlr_viz.png")
 } else {
@@ -2508,7 +2508,7 @@ mlr_pvalue_viz <- visualize(null_distribution_mlr) +
 #}
 ```
 
-```{r shaded-p-values-partial, echo=FALSE, out.width="85%", fig.height=12, fig.cap="Shaded p-values for the partial slopes in this multiple regression."}
+```{r shaded-p-values-partial, echo=FALSE, out.width="55%", fig.height=12, fig.cap="Shaded p-values for the partial slopes in this multiple regression."}
 if(is_latex_output()) {
   knitr::include_graphics("images/mlr_pvalue_viz.png")
 } else {