Merge branch 'master' of github.com:hadley/r4ds

model-basics.Rmd

@@ -239,7 +239,7 @@ These are exactly the same values we got with `optim()`! Behind the scenes `lm()
 
 For simple models, like the one above, you can figure out what pattern the model captures by carefully studying the model family and the fitted coefficients. And if you ever take a statistics course on modelling, you're likely to spend a lot of time doing just that. Here, however, we're going to take a different tack. We're going to focus on understanding a model by looking at its predictions. This has a big advantage: every type of predictive model makes predictions (otherwise what use would it be?) so we can use the same set of techniques to understand any type of predictive model.
 
-It's also useful to see what the model doesn't capture, the so called residuals which are left after subtracting the predictions from the data. Residuals are a powerful because they allow us to use models to remove striking patterns so we can study the subtler trends that remain.
+It's also useful to see what the model doesn't capture, the so called residuals which are left after subtracting the predictions from the data. Residuals are powerful because they allow us to use models to remove striking patterns so we can study the subtler trends that remain.
 
 ### Predictions
 

transform.Rmd

@@ -810,7 +810,7 @@ daily %>%
     
     Which is more important: arrival delay or departure delay?
 
-1.  Come up with another appraoch that will give you the same output as 
+1.  Come up with another approach that will give you the same output as 
     `not_cancelled %>% count(dest)` and 
     `not_cancelled %>% count(tailnum, wt = distance)` (without using 
     `count()`).