Update inf-model-slr.qmd

inf-model-slr.qmd

@@ -21,13 +21,13 @@ Many of the inferential ideas are remarkably similar to those covered in previou
 The technical conditions for linear models are typically assessed graphically, although independence of observations continues to be of utmost importance.
 
 We encourage the reader to think broadly about the models at hand without putting too much dependence on the exact p-values that are reported from the statistical software.
-Inference on models with multiple explanatory variables can suffer from data snooping which result in false positive claims.
+Inference on models with multiple explanatory variables can suffer from data snooping which results in false positive claims.
 We provide some guidance and hope the reader will further their statistical learning after working through the material in this text.
 :::
 
 ```{r}
 #| include: false
-terms_chp_24 <- c("inference with single precictor regression")
+terms_chp_24 <- c("inference with single predictor regression")
 ```
 
 \vspace{-7mm}
@@ -172,7 +172,7 @@ terms_chp_24 <- c(terms_chp_24, "variability of the slope")
 #| fig-asp: 0.5   
 ggplot() +
   geom_point(data = sandwich2, aes(x = ad, y = rev), 
-             size = 3, , shape = 22,
+             size = 3, shape = 22,
              fill = IMSCOL["green", "full"], color = "#FFFFFF") +
   geom_smooth(data = sandwich2, aes(x = ad, y = rev), 
               method = "lm", se = FALSE, fullrange = TRUE, 
@@ -264,7 +264,7 @@ ggplot(sandwich_many_lm, aes(x = estimate)) +
 ```
 
 Recall, the example described in this introduction is hypothetical.
-That is, we created an entire population in order demonstrate how the slope of a line would vary from sample to sample.
+That is, we created an entire population in order to demonstrate how the slope of a line would vary from sample to sample.
 The tools in this textbook are designed to evaluate only one single sample of data.
 With actual studies, we do not have repeated samples, so we are not able to use repeated samples to visualize the variability in slopes.
 We have seen variability in samples throughout this text, so it should not come as a surprise that different samples will produce different linear models.
@@ -880,7 +880,7 @@ Use @tbl-midtermUnempRegTable to determine the p-value for the hypothesis test.
 
 ------------------------------------------------------------------------
 
-The last column of the table gives the p-value for the two-sided hypothesis test for the coefficient of the unemployment rate 0.2961 That is, the data do not provide convincing evidence that a higher unemployment rate has any correspondence with smaller or larger losses for the President's party in the House of Representatives in midterm elections. If there was no linear relationship between the two variables (i.e., if $\beta_1 = 0)$, then we would expect to see linear models as or more extreme that the observed model roughly 30% of the time.
+The last column of the table gives the p-value for the two-sided hypothesis test for the coefficient of the unemployment rate 0.2961. That is, the data do not provide convincing evidence that a higher unemployment rate has any correspondence with smaller or larger losses for the President's party in the House of Representatives in midterm elections. If there was no linear relationship between the two variables (i.e., if $\beta_1 = 0)$, then we would expect to see linear models as or more extreme that the observed model roughly 30% of the time.
 :::
 
 \clearpage