B Concordance with Introduction to Modern Statistics (IMS)

This book is meant to be somewhat aligned pedagogically with part of the book Introduction to Modern Statistics (IMS) by Mine Çetinkaya-Rundel and Johanna Hardin. But it’s not a perfect, one-to-one match. The table below shows the concordance between the two books with some notes that explain when one book does something different from the other.

This book	IMS	Notes
Ch. 1		This book contains a specific introduction to R and RStudio with some basic statistical vocabulary.
	Ch. 1	IMS introduces a lot of vocabulary. This book introduces most of that same vocabulary, but across multiple chapters.
Ch. 2		This book contains a specific introduction to R Markdown.
	Ch. 2	IMS discusses study design and sampling. Some of that information is scattered across multiple chapters of this book, but not all of it. (For example, this book doesn’t get into stratified or cluster sampling.)
	Ch. 3	IMS has “Applications” chapters at the end of each section. In this book, the applications are woven into each chapter.
Ch. 3	Ch. 4	Categorical data.
Ch. 4	Ch. 5	Numerical data.
Ch. 5		This book has a dedicated chapter on manipulating data using dplyr.
	Ch. 6	Applications.
Ch. 6	Ch. 7	Correlation.
Ch. 7	Ch. 7	Simple linear regression.
	Ch. 8	Multiple regression—not covered in this book.
	Ch. 9	Logistic regression—not covered in this book.
	Ch. 10	Applications.
Ch. 8	Ch. 11	Introduction to randomization, Part 1—This book takes four chapters to cover the material that IMS covers in one chapter.
Ch. 9	Ch. 11	Introduction to randomization, Part 2.
Ch. 10	Ch. 11	Hypothesis testing with randomization, Part 1.
Ch. 11	Ch. 11	Hypothesis testing with randomization, Part 2.
Ch. 12	Ch. 12	Confidence intervals.
Ch. 13	Ch. 13	Normal models—This book takes two chapters to cover the material that IMS covers in one chapter.
Ch. 14	Ch. 13	Sampling distribution models.
	Ch. 14	IMS has a chapter on decision errors that was covered in this book back in Ch. 10. It also covers the concept of power, which is not covered in this book.
	Ch. 15	Applications.
Ch. 15	Ch. 16	Inference for one proportion.
Ch. 16	Ch. 17	Inference for two proportions.
Ch. 17		Chi-square goodness-of-fit test. (This is only covered in IMS in a standalone R tutorial appearing in Ch. 23.)
Ch. 18	Ch. 18	Chi-square test for independence.
Ch. 19	Ch. 19	Inference for one mean.
Ch. 20	Ch. 21	Inference for paired data.
Ch. 21	Ch. 20	Inference for two independent means.
Ch. 22	Ch. 22	ANOVA. This is the last chapter of this book.
	Ch. 23	Applications.
	Ch. 24	Inference for linear regression with a single predictor.
	Ch. 25	Inference for linear regression with multiple predictors.
	Ch. 26	Inference for logistic regression.
	Ch. 27	Applications.