diff --git a/model-slr.qmd b/model-slr.qmd
index a016404b..2da19bc2 100644
--- a/model-slr.qmd
+++ b/model-slr.qmd
@@ -33,7 +33,7 @@ However, the prediction would be far from perfect, since other factors play a ro
 #| fig-cap: |
 #|   Requests from twelve separate buyers were simultaneously placed with a 
 #|   trading company to purchase Target Corporation stock (ticker TGT, December 
-#|   28th, 2018), and the total cost of the shares were reported. Because the 
+#|   28th, 2018), and the total cost of the shares was reported. Because the 
 #|   cost is computed using a linear formula, the linear fit is perfect.
 #| fig-alt: |
 #|   A scatterplot showing a perfect linear relationship between number of 
@@ -212,7 +212,7 @@ ggplot(possum, aes(x = total_l, y = head_l)) +
   )
 ```
 
-We want to describe the relationship between head and total length of possum's with a line.
+We want to describe the relationship between head and total length of possums with a line.
 In this example, we will use the total length as the predictor variable, $x,$ to predict a possum's head length, $y.$ We could fit the linear relationship by eye, as in @fig-scattHeadLTotalLLine.
 
 \clearpage
@@ -489,7 +489,7 @@ ggplot(m_head_total_aug, aes(x = .fitted, y = .resid)) +
 \clearpage
 
 ::: {.workedexample data-latex=""}
-One purpose of residual plots is to identify characteristics or patterns still apparent in data after fitting a model.
+One purpose of residual plots is to identify characteristics or patterns still apparent in the data after fitting a model.
 The figure below shows three scatterplots with linear models in the first row and residual plots in the second row.
 Can you identify any patterns in the residuals?