Update layout of chapter introductions to new 2-page style

ch_distributions/TeX/ch_distributions.tex

@@ -1,11 +1,20 @@
-\chapter{Distributions of random variables}
-%\label{modeling}
-\label{ch_distributions}
+\begin{chapterpage}{Distributions of random variables}
+  \chaptertitle[30]{Distributions of random \titlebreak{} variables}
+  \label{ch_distributions}
+  \chaptersection{normalDist}
+  \chaptersection{assessingNormal}
+  \chaptersection{geomDist}
+  \chaptersection{binomialModel}
+  \chaptersection{negativeBinomial}
+  \chaptersection{poisson}
+\end{chapterpage}
 \renewcommand{\chapterfolder}{ch_distributions}
 
-\Comment{A 1-2 paragraph introduction is required.}
 
+\Comment{A 1-2 paragraph introduction is required.}
 
+\chapterintro{Lorum ipsum
+  ...}
 
 %_________________
 \section{Normal distribution}

ch_foundations_for_inf/TeX/ch_foundations_for_inf.tex

@@ -1,24 +1,29 @@
-\chapter{Foundations for inference}
-\label{foundationsForInference}
-\label{ch_foundations_for_inf}
+\begin{chapterpage}{Foundations for inference}
+  \chaptertitle{Foundations for inference}
+  \label{foundationsForInference}
+  \label{ch_foundations_for_inf}
+  \chaptersection{pointEstimates}
+  \chaptersection{confidenceIntervals}
+  \chaptersection{hypothesisTesting}
+\end{chapterpage}
 \renewcommand{\chapterfolder}{ch_foundations_for_inf}
 
-Statistical inference is concerned primarily with
-understanding the uncertainty of parameter estimates.
-While the equations and details change
-depending on the setting, the foundations for inference
-are the same throughout all of statistics.
-We start with a familiar topic:
-the notion of using a sample proportion as our estimate
-of a population proportion.
-Next, we create what's called a
-\hiddenterm{confidence interval}, which is a range
-of values where the true population value is likely to lie.
-Finally, we introduce a hypothesis testing framework,
-which allows us to formally evaluate claims about the
-population, such as \emph{a survey shows a candidate
-has a majority of support} of the voting population
-(whether the proportion that supports is greater than 0.5).
+\chapterintro{Statistical inference is concerned primarily with
+  understanding the uncertainty of parameter estimates.
+  While the equations and details change
+  depending on the setting, the foundations for inference
+  are the same throughout all of statistics.
+  We start with a familiar topic:
+  the notion of using a sample proportion as our estimate
+  of a population proportion.
+  Next, we create what's called a
+  \hiddenterm{confidence interval}, which is a range
+  of values where the true population value is likely to lie.
+  Finally, we introduce a hypothesis testing framework,
+  which allows us to formally evaluate claims about the
+  population, such as \emph{a survey shows a candidate
+  has a majority of support} of the voting population
+  (whether the proportion that supports is greater than 0.5).}
 
 
 
@@ -2393,6 +2398,45 @@ \subsection{Choosing a significance level}
 \index{hypothesis testing|)}
 
 
+\subsection{Statistical significance versus practical significance}
+
+When the sample size becomes larger,
+point estimates become more precise and any real differences
+in the mean and null value become easier to detect and recognize.
+Even a very small difference would likely be detected if we took
+a large enough sample.
+Sometimes researchers will take such large samples that even
+the slightest difference is detected, even differences where
+there is no practical value.
+In such cases, we still say the difference is
+\term{statistically significant},
+but it is not \term{practically significant}.
+
+%Statistically significant differences are sometimes
+%so minor that they are not practically relevant.
+%This is especially important to research:
+%if we conduct a study, we want to focus on finding
+%a meaningful result.
+%We don't want to spend lots of money finding results
+%that hold no practical value.
+
+One role of a data scientist in conducting a study often
+includes planning the size of the study.
+The data scientist might first consult experts or scientific
+literature to learn what would be the smallest meaningful
+difference from the null value.
+She also would obtain other information,
+such as a very rough estimate of the true proportion $p$,
+so that she could roughly estimate the standard error.
+From here, she can suggest a sample size that is sufficiently
+large that, if there is a real difference that is meaningful,
+we could detect it.
+While larger sample sizes may still be used,
+these calculations are especially helpful when considering
+costs or potential risks, such as possible health impacts
+to volunteers in a medical study.
+
+
 \subsection{One-sided hypothesis tests (special topic)}
 
 So far we've only considered what are called \term{two-sided

ch_inference_for_means/TeX/ch_inference_for_means.tex

@@ -1,19 +1,27 @@
-\chapter{Inference for numerical data}
-\label{inferenceForNumericalData}
-\label{ch_inference_for_means}
+\begin{chapterpage}{Inference for numerical data}
+  \chaptertitle{Inference for numerical data}
+  \label{inferenceForNumericalData}
+  \label{ch_inference_for_means}
+  \chaptersection{oneSampleMeansWithTDistribution}
+  \chaptersection{pairedData}
+  \chaptersection{differenceOfTwoMeans}
+  \chaptersection{PowerForDifferenceOfTwoMeans}
+  \chaptersection{anovaAndRegrWithCategoricalVariables}
+\end{chapterpage}
 \renewcommand{\chapterfolder}{ch_inference_for_means}
 
-Chapters~\ref{ch_foundations_for_inf}
-introduced a framework for statistical inference based
-on confidence intervals and hypotheses using the
-normal distribution.
-In this chapter, we encounter several new point estimates
-and a couple new distributions.
-In each case, the inference ideas remain the same:
-determine which point estimate or test statistic is useful,
-identify an appropriate distribution for the point estimate
-or test statistic, and
-apply the ideas from Chapter~\ref{foundationsForInference}.
+
+\chapterintro{Chapters~\ref{ch_foundations_for_inf}
+  introduced a framework for statistical inference based
+  on confidence intervals and hypotheses using the
+  normal distribution.
+  In this chapter, we encounter several new point estimates
+  and a couple new distributions.
+  In each case, the inference ideas remain the same:
+  determine which point estimate or test statistic is useful,
+  identify an appropriate distribution for the point estimate
+  or test statistic, and
+  apply the ideas from Chapter~\ref{foundationsForInference}.}
 
 
 
@@ -199,7 +207,7 @@ \subsection{One sample $t$-confidence intervals}
    {\footnotesize Photo by Mike Baird (\oiRedirect{textbook-bairdphotos_com}{www.bairdphotos.com}). \oiRedirect{textbook-CC_BY_2}{CC~BY~2.0~license}.}\vspace{-8mm}}
    \label{rissosDolphin}
 \end{minipage}
-\vspace{3mm}
+\stdvspace{}
 \end{figure}
 \setlength{\captionwidth}{\mycaptionwidth}
 

ch_inference_for_props/TeX/ch_inference_for_props.tex

@@ -1,21 +1,26 @@
-\chapter{Inference for categorical data}
-\label{inferenceForCategoricalData}
-\label{ch_inference_for_props}
+\begin{chapterpage}{Inference for categorical data}
+  \chaptertitle{Inference for categorical data}
+  \label{inferenceForCategoricalData}
+  \label{ch_inference_for_props}
+  \chaptersection{singleProportion}
+  \chaptersection{differenceOfTwoProportions}
+  \chaptersection{oneWayChiSquare}
+  \chaptersection{twoWayTablesAndChiSquare}
+\end{chapterpage}
 \renewcommand{\chapterfolder}{ch_inference_for_props}
 
-Chapter~\ref{inferenceForCategoricalData}
-introduces inference in the setting of categorical data.
-The methods we learned in Chapter~\ref{ch_foundations_for_inf}
-will be useful in each of these settings.
-For example, while a sample proportion can be
-well-characterized by a nearly normal distribution
-when certain conditions are satisfied,
-this is also true when looking at the difference
-of two proportions when conditions are satisfied.
-In other instances, such as those with contingency tables,
-we will use a different distribution, though the core
-ideas remain the same.
-
+\chapterintro{In this chapter,
+we apply the methods and ideas from Chapter~\ref{ch_foundations_for_inf}
+in several contexts for categorical data.
+We'll start by revisiting what we learned for a single
+proportion, where the normal distribution can be used
+to model the uncertainty in the sample proportion.
+Next, we apply these same ideas, using the normal
+model to analyze the difference of two proportions
+when conditions are satisfied.
+Later in the chapter we will encounter contingency tables,
+and we will use a different distribution, though the core
+ideas of hypothesis testing remain the same.}
 
 
 %__________________

ch_intro_to_data/TeX/ch_intro_to_data.tex

@@ -1,19 +1,26 @@
-\chapter{Introduction to data}
-\label{introductionToData}
-\label{ch_intro_to_data}
+\begin{chapterpage}{Introduction to data}
+  \chaptertitle{Introduction to data}
+  \label{introductionToData}
+  \label{ch_intro_to_data}
+  \chaptersection{basicExampleOfStentsAndStrokes}
+  \chaptersection{dataBasics}
+  \chaptersection{overviewOfDataCollectionPrinciples}
+  \chaptersection{section_obs_data_sampling}
+  \chaptersection{experimentsSection}
+\end{chapterpage}
 \renewcommand{\chapterfolder}{ch_intro_to_data}
 
 %\begin{tipBox}{\tipBoxTitle[Chapter Goal:]{Thinking about data}
 %Understand basics about data organization, data types, numerical summaries of data, graphical summaries of data, and foundational techniques for data collection. We begin and end the chapter with case studies.}
 %\end{tipBox}
 
-Scientists seek to answer questions using rigorous methods
-and careful observations.
-These observations -- collected from the likes of field notes,
-surveys, and experiments -- form the backbone of a statistical
-investigation and are called \term{data}.
-Statistics is the study of how best to collect, analyze,
-and draw conclusions from data, %It is helpful to put statistics in the context of a general process of investigation:
+\chapterintro{Scientists seek to answer questions
+  using rigorous methods and careful observations.
+  These observations -- collected from the likes of field notes,
+  surveys, and experiments -- form the backbone of a statistical
+  investigation and are called \term{data}.
+  Statistics is the study of how best to collect, analyze,
+  and draw conclusions from data, %It is helpful to put statistics in the context of a general process of investigation:
 %\begin{enumerate}
 %\setlength{\itemsep}{0mm}
 %\item Identify a question or problem.
@@ -23,12 +30,14 @@ \chapter{Introduction to data}
 %%\item Make decisions based on the conclusion.
 %\end{enumerate}
 %Statistics as a subject focuses on making stages 2-4 objective, rigorous, and efficient. That~is, statistics has three primary components: How best can we collect data? How should it be analyzed? And what can we infer from the analysis?
-and in this first chapter,
-we focus on both the properties of data
-and on the collection of data.
+  and in this first chapter,
+  we focus on both the properties of data
+  and on the collection of data.}
 
 %The topics scientists investigate are as diverse as the questions they ask. However, many of these investigations can be addressed with a small number of data collection techniques, analytic tools, and fundamental concepts in statistical inference. This chapter provides a glimpse into these and other themes we will encounter throughout the rest of the book. We introduce the basic principles of each branch and learn some tools along the way. We will encounter applications from other fields, some of which are not typically associated with science but nonetheless can benefit from statistical study.
 
+
+
 \section{Case study: using stents to prevent strokes}
 \label{basicExampleOfStentsAndStrokes}
 

ch_probability/TeX/ch_probability.tex

@@ -1,17 +1,24 @@
-\chapter{Probability}
-\label{probability}
-\label{ch_probability}
+\begin{chapterpage}{Probability}
+  \chaptertitle{Probability}
+  \label{probability}
+  \label{ch_probability}
+  \chaptersection{basicsOfProbability}
+  \chaptersection{conditionalProbabilitySection}
+  \chaptersection{smallPop}
+  \chaptersection{randomVariablesSection}
+  \chaptersection{contDist}
+\end{chapterpage}
 \renewcommand{\chapterfolder}{ch_probability}
 
 \index{probability|(}
 
 \Comment{Need to improve the wording of this chapter intro.}
 
-Probability forms a foundation for statistics,
-and you might already be familiar with many of the ideas.
-However, formalization of the concepts is new for most.
-This chapter aims to introduce probability concepts using
-many examples that will be familiar to most people.
+\chapterintro{Probability forms a foundation for statistics,
+  and you might already be familiar with many of the ideas.
+  However, formalization of the concepts is new for most.
+  This chapter aims to introduce probability concepts using
+  many examples that will be familiar to most people.}
 
 
 

ch_regr_mult_and_log/TeX/ch_regr_mult_and_log.tex

@@ -1,10 +1,23 @@
-\chapter{Multiple and logistic regression}
-\label{multipleRegressionAndANOVA}
-\label{multipleAndLogisticRegression}
-\label{ch_regr_mult_and_log}
+\begin{chapterpage}{Multiple and logistic regression}
+  \chaptertitle{Multiple and logistic \titlebreak{} regression}
+  \label{multipleRegressionAndANOVA}
+  \label{multipleAndLogisticRegression}
+  \label{ch_regr_mult_and_log}
+  \chaptersection{introductionToMultipleRegression}
+  \chaptersection{model_selection_section}
+  \chaptersection{multipleRegressionModelAssumptions}
+  \chaptersection{logisticRegression}
+\end{chapterpage}
 \renewcommand{\chapterfolder}{ch_regr_mult_and_log}
 
-The principles of simple linear regression lay the foundation for more sophisticated regression methods used in a wide range of challenging settings. In Chapter~\ref{multipleAndLogisticRegression}, we explore multiple regression, which introduces the possibility of more than one predictor, and logistic regression, a technique for predicting categorical outcomes with two possible categories.
+\chapterintro{The principles of simple linear regression
+  lay the foundation for more sophisticated regression
+  methods used in a wide range of challenging settings.
+  In Chapter~\ref{multipleAndLogisticRegression},
+  we explore multiple regression, which introduces the
+  possibility of more than one predictor, and logistic
+  regression, a technique for predicting categorical
+  outcomes with two possible categories.}
 
 
 
@@ -1914,7 +1927,7 @@ \section{Introduction to logistic regression}
 as a tool for building models when there is a categorical
 response variable with two levels, e.g. yes and no.
 Logistic regression is a type of
-\term{generalized linear model} (\indexthis{GLM})
+\term{generalized linear model} (\term{GLM})
 for response variables
 where regular multiple regression does not work very well.
 In particular, the response variable in these settings often

ch_regr_simple_linear/TeX/ch_regr_simple_linear.tex

@@ -1,16 +1,24 @@
-\chapter{Introduction to linear regression}
-\label{linRegrForTwoVar}
-\label{ch_regr_simple_linear}
+\begin{chapterpage}{Introduction to linear regression}
+  \chaptertitle{Introduction to linear \titlebreak{} regression}
+  \label{linRegrForTwoVar}
+  \label{ch_regr_simple_linear}
+  \chaptersection{fitting_line_to_data_section}
+  \chaptersection{fittingALineByLSR}
+  \chaptersection{typesOfOutliersInLinearRegression}
+  \chaptersection{inferenceForLinearRegression}
+\end{chapterpage}
 \renewcommand{\chapterfolder}{ch_regr_simple_linear}
 
+
 \index{linear regression|textbf}
 
-Linear regression is a very powerful statistical technique.
-Many people have some familiarity with regression just from
-reading the news, where graphs with straight lines are overlaid
-on scatterplots.
-Linear models can be used for prediction or to evaluate whether
-there is a linear relationship between two numerical variables.
+\chapterintro{Linear regression is a very powerful
+  statistical technique.
+  Many people have some familiarity with regression just from
+  reading the news, where graphs with straight lines are overlaid
+  on scatterplots.
+  Linear models can be used for prediction or to evaluate whether
+  there is a linear relationship between two numerical variables.}