Fix references in ht chp (#349)

* Fix table formatting mostly

* Fix section IDs

* Fix refs

Author: mine-cetinkaya-rundel

01-data-hello.qmd

@@ -18,7 +18,7 @@ In this first chapter, we focus on both the properties of data and on the collec
 terms_chp_1 <- c("data")
 ```
 
-## Case study: Using stents to prevent strokes {#sec-case-study-stents-strokes}
+## Case study: Using stents to prevent strokes {sec-case-study-stents-strokes}
 
 In this section we introduce a classic challenge in statistics: evaluating the efficacy of a medical treatment.
 Terms in this section, and indeed much of this chapter, will all be revisited later in the text.
@@ -145,7 +145,7 @@ This study looked at patients with very specific characteristics who volunteered
 In addition, there are many types of stents, and this study only considered the self-expanding Wingspan stent (Boston Scientific).
 However, this study does leave us with an important lesson: we should keep our eyes open for surprises.
 
-## Data basics {#sec-data-basics}
+## Data basics {sec-data-basics}
 
 Effective presentation and description of data is a first step in most analyses.
 This section introduces one structure for organizing data as well as some terminology that will be used throughout this book.
@@ -298,7 +298,7 @@ county_var_def |>
 The [`county`](http://openintrostat.github.io/usdata/reference/county.html) data can be found in the [**usdata**](http://openintrostat.github.io/usdata) R package.
 :::
 
-### Types of variables {#variable-types}
+### Types of variables {variable-types}
 
 Examine the `unemployment_rate`, `pop2017`, `state`, and `median_edu` variables in the `county` dataset.
 Each of these variables is inherently different from the other three, yet some share certain characteristics.
@@ -402,7 +402,7 @@ Classify each variable as either numerical or categorical?[^01-data-hello-5]
 [^01-data-hello-5]: The `group` variable can take just one of two group names, making it categorical.
     The `num_migraines` variable describes a count of the number of migraines, which is an outcome where basic arithmetic is sensible, which means this is a numerical outcome; more specifically, since it represents a count, `num_migraines` is a discrete numerical variable.
 
-### Relationships between variables {#variable-relations}
+### Relationships between variables {variable-relations}
 
 Many analyses are motivated by a researcher looking for a relationship between two or more variables.
 A social scientist may like to answer some of the following questions:
@@ -603,7 +603,7 @@ We will revisit this idea when we discuss experiments later in the book.
 
 \vspace{10mm}
 
-## Chapter review {#chp1-review}
+## Chapter review {chp1-review}
 
 ### Summary
 
@@ -625,7 +625,7 @@ make_terms_table(terms_chp_1)
 
 \clearpage
 
-## Exercises {#chp1-exercises}
+## Exercises {chp1-exercises}
 
 Answers to odd-numbered exercises can be found in [Appendix -@sec-exercise-solutions-01].
 

11-foundations-randomization.qmd

@@ -1,4 +1,4 @@
-# Decision Errors {#decerr}
+# Decision Errors {#sec-decerr}
 
 ```{r}
 #| include: false

14-foundations-errors.qmd

@@ -4,7 +4,7 @@
 source("_common.R")
 ```
 
-# Inference for a single proportion {#inference-one-prop}
+# Inference for a single proportion {#sec-inference-one-prop}
 
 ::: {.chapterintro data-latex=""}
 Focusing now on statistical inference for categorical data, we will revisit many of the foundational aspects of hypothesis testing from Chapter \@ref(foundations-randomization).