diff --git a/model-basics.Rmd b/model-basics.Rmd
index e8e0433..56a056a 100644
--- a/model-basics.Rmd
+++ b/model-basics.Rmd
@@ -324,7 +324,7 @@ The following sections explore how this plays out in more detail.
 
 ### Categorical variables
 
-Generating a function from a formula is straight forward when the predictor is continuous, but things get a bit more complicated when the predictor is categorical. Imagine you have a formula like `y ~ sex`, where sex could either be male or female. It doesn't make sense to convert that to a formula like `y = x_0 + x_1 * sex` because `sex` isn't a number - you can't multiply it! Instead what R does is convert it to `y = x_0 + x_1 * sex_male` where `sex_male` is one if `sex` is male and 0 otherwise.
+Generating a function from a formula is straight forward when the predictor is continuous, but things get a bit more complicated when the predictor is categorical. Imagine you have a formula like `y ~ sex`, where sex could either be male or female. It doesn't make sense to convert that to a formula like `y = x_0 + x_1 * sex` because `sex` isn't a number - you can't multiply it! Instead what R does is convert it to `y = x_0 + x_1 * sex_male` where `sex_male` is one if `sex` is male and zero otherwise.
 
 If you want to see what R actually does, you can use the `model.matrix()` function. It takes similar inputs to `lm()` but returns the numeric matrix that R uses to fit the model. This is useful if you ever want to understand exactly which equation is generated by your formula.
 
@@ -360,7 +360,7 @@ grid <- sim2 %>%
 grid
 ```
 
-Effectively, a model with a categorical `x` will predict the mean value for each category. (Why? Because the mean minimise the root-mean-squared distance.) That's easy to see if we overlay the predictions on top of the original data:
+Effectively, a model with a categorical `x` will predict the mean value for each category. (Why? Because the mean minimises the root-mean-squared distance.) That's easy to see if we overlay the predictions on top of the original data:
 
 ```{r}
 ggplot(sim2, aes(x)) + 
@@ -400,7 +400,7 @@ To visualise these models we need two new tricks:
    
 1.  To generate predictions from both models simultaneously, we can use 
     `gather_predictions()` which adds each prediction as a row. The
-    complete of `gather_predictions()` is `spread_predictions()` which adds 
+    complement of `gather_predictions()` is `spread_predictions()` which adds 
     each prediction to a new column.
     
 Together this gives us:
@@ -539,9 +539,9 @@ Here we've focused on linear models, which is a fairly limited space (but it doe
 Some extensions of linear models are:
 
 * Generalised linear models, e.g. `stats::glm()`. Linear models assume that
-  the predictor is continuous and the errors has a normal distribution. 
+  the response is continuous and the error has a normal distribution. 
   Generalised linear models extend linear models to include non-continuous
-  predictors (e.g. binary data or counts). They work by defining a distance
+  responses (e.g. binary data or counts). They work by defining a distance
   metric based on the statistical idea of likelihood.
   
 * Generalised additive models, e.g. `mgcv::gam()`, extend generalised