Make 4 backticks -> 3 in code chunks

exercises/_05-ex-explore-numerical.qmd

@@ -71,7 +71,7 @@ Indicate which of the plots show (a) a positive association, (b) a negative as
         axis.text = element_blank()
       ) +
       labs(x = NULL, y = NULL)
-    ````
+    ```
 
 1. **Reproducing bacteria.** 
 Suppose that there is only sufficient space and nutrients to support one million bacterial cells in a petri dish. 
@@ -504,7 +504,7 @@ Also shown below is a spatial intensity map of the same data.^[The [`county_comp
       coord_quickmap()
 
     p_hist + p_map + plot_layout(widths = c(1, 2))
-    ````
+    ```
 
     a.  Describe the numerical distribution and comment on whether a log transformation may be advisable for these data.
 
@@ -574,7 +574,7 @@ The US census collects data on race and ethnicity of Americans, among many other
     p_hist + p_hist_log
 
     p_map
-    ````
+    ```
 
     a.  Describe the numerical distribution and comment on why we might want to use log-transformed values in analyzing or modeling these data.
 

exercises/_08-ex-model-mlr.qmd

@@ -178,7 +178,7 @@ The plots below show the predicted ROI vs. actual ROI for each of the genres sep
       theme_minimal() + 
       labs(x = "Actual ROI", y = "Predicted ROI", color = "Genre") +
       scale_color_openintro("five")
-    ````
+    ```
 
 1. **Predicting baby weights.**
 A more realistic approach to modeling baby weights is to consider all possibly related variables at once. Other variables of interest include length of pregnancy in weeks (`weeks`), mother's age in years (`mage`), the sex of the baby (`sex`), smoking status of the mother (`habit`), and the number of hospital (`visits`) visits during pregnancy. Below are three observations from this data set.
@@ -402,7 +402,8 @@ Below are the six models we evaluate and their adjusted $R^2$ values. [@data:bir
       glance() %>%
       pull(adj.r.squared) %>%
       round(3)
-    ````
+    ```
+
     -  Predict `weight` from `mature`: `r m_mature`
     -  Predict `weight` from `weeks`:  `r m_weeks`
     -  Predict `weight` from `visits`: `r m_visits`
@@ -441,7 +442,8 @@ Using body measurement and other relevant data on three species (*Adelie*, *Chin
       glance() %>%
       pull(adj.r.squared) %>%
       round(3)
-    ````
+    ```
+
     -  Predict body mass from `bill_length_mm`:    `r m_bl`
     -  Predict body mass from `bill_depth_mm`:     `r m_bd`
     -  Predict body mass from `flipper_length_mm`: `r m_fl`

exercises/_18-ex-inference-tables.qmd

@@ -155,7 +155,7 @@ In order to assess whether habitat conditions are related to the sunlight choice
       add_header_above(c(" "=1, "sunlight" = 3, " " = 1)) %>%
       column_spec(1:5, width = "5em") %>%
       add_header_above(c("Original data" = 5))
-    ````
+    ```
 
     Then, the data were randomized once, where sunlight preference was randomly assigned to the lizards across different sites. The results of the randomization is shown below.
 

exercises/_20-ex-inference-two-means.qmd

@@ -366,7 +366,7 @@ Below are some summary statistics from this dataset along with box plots showing
       labs(y = "Feed type", x = "Weight (in grams)")    
 
     p_box + p_hist
-    ````
+    ```
 
     ::: {.content-hidden unless-format="pdf"}
     *See next page for parts a to d.*
@@ -442,7 +442,7 @@ If your hypothesis test yields a statistically significant result, discuss wheth
       kable_styling(bootstrap_options = c("striped", "condensed"), 
                     latex_options = "HOLD_position",
                     full_width = FALSE)
-    ````
+    ```
 
     \clearpage
 

exercises/_24-ex-inf-model-slr.qmd

@@ -561,7 +561,7 @@ Many people believe that weight, drinking habits, and many other factors are muc
 1. **Urban homeowners, conditions.**
 The scatterplot below shows the percent of families who own their home vs. the percent of the population living in urban areas. [@data:urbanOwner] There are 52 observations, each corresponding to a state in the US. Puerto Rico and District of Columbia are also included.
 
-    ````{r}
+    ```{r}
     #| out.width: 50%
     #| fig.asp: 1.0
     library(openintro)
@@ -576,7 +576,7 @@ The scatterplot below shows the percent of families who own their home vs. the p
        cex.lab = 1.5, cex.axis = 1.5)
 
     rsq_uo <- round(cor(urban_owner$poppct_urban,  urban_owner$pct_owner_occupied), 2)^2*100
-    ````
+    ```
 
     a. For these data, $R^2$ is `r rsq_uo`%. What is the value of the correlation coefficient? How can you tell if it is positive or negative?