From 4b505b0f86d9cd65b2a81f8301a80a1cc4130bdf Mon Sep 17 00:00:00 2001 From: Colin Gillespie Date: Mon, 3 Oct 2016 13:12:08 +0100 Subject: [PATCH] Remove extra "we" (#335) --- EDA.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/EDA.Rmd b/EDA.Rmd index fa3725c..f13ab71 100644 --- a/EDA.Rmd +++ b/EDA.Rmd @@ -540,7 +540,7 @@ ggplot(data = faithful) + Patterns provide one of the most useful tools for data scientists because they reveal covariation. If you think of variation as a phenomenon that creates uncertainty, covariation is a phenomenon that reduces it. If two variables covary, you can use the values of one variable to make better predictions about the values of the second. If the covariation is due to a causal relationship (a special case), then you can use the value of one variable to control the value of the second. -Models are a tool for extracting patterns out of data. For example, consider the diamonds data. It's hard to understand the relationship between cut and price, because cut and carat, and carat and price are tightly related. It's possible to use a model to remove the very strong relationship between price and carat so we we can explore the subtleties that remain: +Models are a tool for extracting patterns out of data. For example, consider the diamonds data. It's hard to understand the relationship between cut and price, because cut and carat, and carat and price are tightly related. It's possible to use a model to remove the very strong relationship between price and carat so we can explore the subtleties that remain: ```{r, dev = "png"} library(modelr)