Mine Çetinkaya-Rundel and Johanna Hardin
|Digital versions||PDF and HTML (online)|
|Source available||Yes, on GitHub|
|Solutions||Odd numbered problems|
|Solution Manual||Available to verified teachers|
|License||Creative Commons Attribution-ShareAlike 3.0|
- First edition (June 2021)
- Black and white paperback version from Amazon $20
- Free desk copy, solution manual, sample exams, problem sets for verified teachers
- Companion data sets available on website
- Labs based on freely available R and RStudio
- For more information and to download
- Wholesale bookstore options
This book is another excellent open source text from the OpenIntro group and is based on OpenIntro Statistics and Introduction to Statistics with Randomization and Simulation by Diez, Barr, and Çetinkaya-Rundel. It strongly emphasizes exploratory data analysis and simulation-based inference with randomization and bootstrapping.
In addition to the exercises at the end of each section and chapter, there are examples and “Guided Practice Questions” for the reader to do with answers to give immediate feedback.
There are 27 chapters grouped into six sections.
- Part 1: Introduction to data. Data structures, variables, summaries, graphics, and basic data collection and study design techniques.
- Part 2: Exploratory data analysis. Data visualization and summarisation, with particular emphasis on multivariable relationships.
- Part 3: Regression modeling. Modeling numerical and categorical outcomes with linear and logistic regression and using model results to describe relationships and made predictions.
- Part 4: Foundations for inference. Case studies are used to introduce the ideas of statistical inference with randomization tests, bootstrap intervals, and mathematical models.
- Part 5: Statistical inference. Further details of statistical inference using randomization tests, bootstrap intervals, and mathematical models for numerical and categorical data.
- Part 6: Inferential modeling. Extending inference techniques presented thus-far to linear and logistic regression settings and evaluating model performance.