diff --git a/05-descriptive-analysis.Rmd b/05-descriptive-analysis.Rmd
index 8e24d11..4f7e613 100644
--- a/05-descriptive-analysis.Rmd
+++ b/05-descriptive-analysis.Rmd
@@ -449,7 +449,7 @@ recs_des %>%
   mutate(p = p * 100)
 ```
 
-`r .preg %>% filter(Region=="Northeast") %>% pull(p) %>% signif(3)`% of the households are in the Northeast, `r .preg %>% filter(Region=="Midwest") %>% pull(p) %>% signif(3)`% in the Midwest, and so on. Note that the proportions in column `p` add up to one.
+`r .preg %>% filter(Region=="Northeast") %>% pull(p) %>% signif(3)`% of the households are in the Northeast, `r .preg %>% filter(Region=="Midwest") %>% pull(p) %>% signif(3)`% are in the Midwest, and so on. Note that the proportions in column `p` add up to one.
 
 \index{Categorical data|(}
 The `survey_prop()` function is essentially the same as using `survey_mean()` with a categorical variable and without specifying a numeric variable in the `x` argument. The following code gives us the same results as above: