Julie Vu and David Harrington

Digital versions Two PDF versions: full screen or tablet
LaTeX source available     Yes
Exercises Yes
Solutions Odd numbered problems
Solution Manual Coming soon, restricted to verified teachers
License Creative Commons Attribution-ShareAlike 3.0

This book is based on OpenIntro Statistics (also on the Approved List) but with a substantial amount of added material containing interesting examples from medicine and the life sciences, along with necessary background material to understand the context. The authors write in the preface, “Computing is an essential part of the practice of statistics. Nearly everyone entering the biomedical sciences will need to interpret the results of analyses conducted in software; many will also need to be capable of conducting such analyses. The text and associated materials separate those two activities to allow students and instructors to emphasize either or both skills.” The self-paced learning labs introduce students to the use of software for data analysis.

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.

Chapter Summaries

  1. Introduction to data. Data structures, basic data collection principles, numerical and graphical summaries, and exploratory data analysis.
  2. Summarizing data. Data summaries, graphics, and a teaser of inference using randomization
  3. Probability. The basic principles of probability.
  4. Distributions of random variables. Introduction to random variables, distributions of discrete
    and continuous random variables, and distributions for pairs of random variables.
  5. Foundations for inference. General ideas for statistical inference in the context of estimating a population mean.
  6. Inference for numerical data. Inference for one-sample and two-sample means with the t-distribution, power calculations for a difference of means, and ANOVA.
  7. Simple linear regression. An introduction to linear regression with a single explanatory variable, evaluating model assumptions, and inference in a regression context.
  8. Multiple linear regression. General multiple regression model, categorical predictors with more than two values, interaction, and model selection.
  9. Inference for categorical data. Inference for single proportions, inference for two or more groups, and outcome-based sampling.