From 66394d2dcdda2e115f3b0a06355b5023c7164d62 Mon Sep 17 00:00:00 2001
From: Mitsuo Shiota <48662507+mitsuoxv@users.noreply.github.com>
Date: Mon, 17 Apr 2023 21:06:06 +0900
Subject: [PATCH] Fix/EDA typos (#1427)

---
 EDA.qmd | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/EDA.qmd b/EDA.qmd
index d31973b..e34c4ee 100644
--- a/EDA.qmd
+++ b/EDA.qmd
@@ -399,7 +399,7 @@ In the exercises, you'll be challenged to figure out why.
 `cut` is an ordered factor: fair is worse than good, which is worse than very good and so on.
 Many categorical variables don't have such an intrinsic order, so you might want to reorder them to make a more informative display.
 One way to do that is with `fct_reorder()`.
-You'll learn more about that function in @sec-modifying-factor-order, but we wanted to give you a quick preview here because it's so useful.
+You'll learn more about that function in @sec-modifying-factor-order, but we want to give you a quick preview here because it's so useful.
 For example, take the `class` variable in the `mpg` dataset.
 You might be interested to know how highway mileage varies across classes:
 
@@ -710,7 +710,7 @@ We're not discussing modelling in this book because understanding what models ar
 ## Summary
 
 In this chapter you've learned a variety of tools to help you understand the variation within your data.
-You've seen technique that work with a single variable at a time and with a pair of variables.
-This might seem painful restrictive if you have tens or hundreds of variables in your data, but they're foundation upon which all other techniques are built.
+You've seen techniques that work with a single variable at a time and with a pair of variables.
+This might seem painfully restrictive if you have tens or hundreds of variables in your data, but they're foundation upon which all other techniques are built.
 
 In the next chapter, we'll focus on the tools we can use to communicate our results.