diff --git a/model-basics.Rmd b/model-basics.Rmd
index 04261a4..361a67e 100644
--- a/model-basics.Rmd
+++ b/model-basics.Rmd
@@ -705,11 +705,11 @@ This chapter has focussed exclusively on the class of linear models, which assum
 * __Generalised additive models__, e.g. `mgcv::gam()`, extend generalised
   linear models to incorporate arbitrary smooth functions. That means you can
   write a formula like `y ~ s(x)` which becomes an equation like 
-  `y = f(x)` and the `gam()` estimate what that function is (subject to some
+  `y = f(x)` and let `gam()` estimate what that function is (subject to some
   smoothness constraints to make the problem tractable).
   
 * __Penalised linear models__, e.g. `glmnet::glmnet()`, add a penalty term to
-  the distance which penalises complex models (as defined by the distance 
+  the distance that penalises complex models (as defined by the distance 
   between the parameter vector and the origin). This tends to make
   models that generalise better to new datasets from the same population.
 
@@ -718,8 +718,8 @@ This chapter has focussed exclusively on the class of linear models, which assum
   of outliers, at the cost of being not quite as good when there are no 
   outliers.
   
-* __Trees__, e.g. `rpart::rpart()`, attack the problem in a complete different
-  way to linear models. They fit a piece-wise constant model, splitting the
+* __Trees__, e.g. `rpart::rpart()`, attack the problem in a completely different
+  way than linear models. They fit a piece-wise constant model, splitting the
   data into progressively smaller and smaller pieces. Trees aren't terribly
   effective by themselves, but they are very powerful when used in aggregate
   by models like __random forests__ (e.g. `randomForest::randomForest()`) or