predictive to predictive question (and similar change elsewhere)

Author: trevorcampbell

intro.Rmd

@@ -77,12 +77,12 @@ all of which are defined in Table \@ref(tab:questions-table).
 Carefully formulating a question as early as possible in your analysis—and 
 correctly identifying which type of question it is—will guide your overall approach to 
 the analysis as well as the selection of appropriate tools.\index{question!data analysis}
-\index{descriptive!definition}
-\index{exploratory!definition}
-\index{predictive!definition}
-\index{inferential!definition}
-\index{causal!definition}
-\index{mechanistic!definition}
+\index{descriptive question!definition}
+\index{exploratory question!definition}
+\index{predictive question!definition}
+\index{inferential question!definition}
+\index{causal question!definition}
+\index{mechanistic question!definition}
 
 Table: (\#tab:questions-table) Types of data analysis question. From [What is the question?](https://science.sciencemag.org/content/347/6228/1314) [@leek2015question] and [The Art of Data Science](https://leanpub.com/artofdatascience) [@peng2015art].
 
@@ -157,9 +157,9 @@ Since we are using R for data analysis in this book, the first step for us is to
 load the data into R. When we load tabular data into
 R, it is represented as a *data frame* object\index{data frame!overview}. Figure
 \@ref(fig:img-spreadsheet-vs-dataframe) shows that an R data frame is very similar
-to a spreadsheet. We refer to the rows as **observations** \index{observation}; these are the things that we
-collect the data on, e.g., voters, cities, etc. We refer to the columns as
-**variables** \index{variable}; these are the characteristics of those observations, e.g., voters' political
+to a spreadsheet. We refer to the rows as \index{observation} **observations**; these are the things that we
+collect the data on, e.g., voters, cities, etc. We refer to the columns as \index{variable}
+**variables**; these are the characteristics of those observations, e.g., voters' political
 affiliations, cities' populations, etc. 
 
 
@@ -479,7 +479,7 @@ ggplot(ten_lang, aes(x = language, y = mother_tongue)) +
 > time, a single expression in R must be contained in a single line of code.
 > However, there *are* a small number of situations in which you can have a
 > single R expression span multiple lines. Above is one such case: here, R knows that a line cannot
-> end with a `+` symbol, and so it keeps reading the next line to figure out
+> end with a `+` symbol, \index{plussymb@$+$} and so it keeps reading the next line to figure out
 > what the right hand side of the `+` symbol should be.  We could, of course,
 > put all of the added layers on one line of code, but splitting them across
 > multiple lines helps a lot with code readability. \index{multi-line expression}

regression1.Rmd

@@ -32,7 +32,7 @@ By the end of the chapter, readers will be able to:
 
 ## The regression problem
 
-Regression, like classification, is a predictive \index{predictive} problem setting where we want
+Regression, like classification, is a predictive \index{predictive question} problem setting where we want
 to use past information to predict future observations. But in the case of
 regression, the goal is to predict *numerical* values instead of *categorical* values. 
 The variable that you want to predict is often called the *response variable*. \index{response variable}