From f4683fce1ee7a21264203178f263c9a5923c65a7 Mon Sep 17 00:00:00 2001 From: Jacob Kaplan Date: Wed, 20 Jun 2018 05:03:40 -0400 Subject: [PATCH] fixes minor spelling mistakes (#631) --- model-many.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model-many.Rmd b/model-many.Rmd index 9ed8827..555e547 100644 --- a/model-many.Rmd +++ b/model-many.Rmd @@ -15,7 +15,7 @@ In this chapter you're going to learn three powerful ideas that help you to work because once you have tidy data, you can apply all of the techniques that you've learned about earlier in the book. -We'll start by diving into a motivating example using data about life expectancy around the world. It's a small dataset but it illustrates how important modelling can be for improving your visualisations. We'll use a large number of simple models to partition out some of the strongest signal so we can see the subtler signals that remain. We'll also see how model summaries can help us pick out outliers and unusual trends. +We'll start by diving into a motivating example using data about life expectancy around the world. It's a small dataset but it illustrates how important modelling can be for improving your visualisations. We'll use a large number of simple models to partition out some of the strongest signals so we can see the subtler signals that remain. We'll also see how model summaries can help us pick out outliers and unusual trends. The following sections will dive into more detail about the individual techniques: