melissa comments addressed

Author: trevorcampbell

clustering.Rmd

@@ -889,7 +889,7 @@ not_standardized_data
 ```
 
 And then we apply the `scale` function to every column in the data frame 
-using `mutate` + `across`.
+using `mutate` and `across`.
 
 ```{r 10-mapdf-scale-data}
 standardized_data <- not_standardized_data |>

inference.Rmd

@@ -90,14 +90,14 @@ knitr::include_graphics("img/population_vs_sample.png")
 Note that proportions are not the *only* kind of population parameter we might
 be interested in. For example, suppose an undergraduate student studying at the University
 of British Columbia in Canada is looking for an apartment
-to rent. They need to create a budget, so they want to know something about
-studio apartment rental prices in Vancouver, BC. This student might 
-formulate the following question:
+to rent. They need to create a budget, so they want to know about
+studio apartment rental prices in Vancouver. This student might 
+formulate the question:
 
-*What is the average price-per-month of studio apartment rentals in Vancouver, Canada?*
+*What is the average price per month of studio apartment rentals in Vancouver?*
 
 In this case, the population consists of all studio apartment rentals in Vancouver, and the
-population parameter is the *average price-per-month*. Here we used the average
+population parameter is the *average price per month*. Here we used the average
 as a measure of the center to describe the "typical value" of studio apartment
 rental prices. But even within this one example, we could also be interested in
 many other population parameters. For instance, we know that not every studio
@@ -1148,9 +1148,9 @@ boot_est_dist +
 
 To finish our estimation of the population parameter, we would report the point
 estimate and our confidence interval's lower and upper bounds. Here the sample
-mean price-per-night of 40 Airbnb listings was 
+mean price per night of 40 Airbnb listings was 
 \$`r round(mean(one_sample$price),2)`, and we are 95\% "confident" that the true
-population mean price-per-night for all Airbnb listings in Vancouver is between
+population mean price per night for all Airbnb listings in Vancouver is between
 \$(`r round(bounds[1],2)`, `r round(bounds[2],2)`).
 Notice that our interval does indeed contain the true
 population mean value, \$`r round(mean(airbnb$price),2)`\! However, in

jupyter.Rmd

@@ -106,7 +106,7 @@ activated by highlighting it with a blue rectangle to its left. After the cell
 has been activated (Figure \@ref(fig:activate-and-run-button)), the cell can be run by either pressing the **Run** (`r fa("play", height = "11px")`) 
 button in the toolbar, or by using the keyboard shortcut `Shift + Enter`.
 
-```{r activate-and-run-button, echo = FALSE, fig.cap = "An activated cell that is ready to be run. The red arrow points to the blue rectangle to the cell's left. The blue rectangle indicates that it is ready to be run. This can be done by clicking the run button (circled in red).", fig.retina = 2, out.width="100%"}
+```{r activate-and-run-button, echo = FALSE, fig.cap = "An activated cell that is ready to be run. The blue rectangle to the cell's left (annotated by a red arrow) indicates that it is ready to be run. The cell can be run by clicking the run button (circled in red).", fig.retina = 2, out.width="100%"}
 image_read("img/activate-and-run-button-annotated.png") |> 
   image_crop("3632x900")
 ```