Stanton J.M. Reasoning with data: An Introduction to Traditional and Bayesian Statistics Using R

The Guilford Press, New York, London, 2017. — 327 p. — ISBN: 9781462530267.Engaging and accessible, this book teaches readers how to use inferential statistical thinking to check their assumptions, assess evidence about their beliefs, and avoid overinterpreting results that may look more promising than they really are. It provides step-by-step guidance for using both classical (frequentist) and Bayesian approaches to inference. Statistical techniques covered side by side from both frequentist and Bayesian approaches include hypothesis testing, replication, analysis of variance, calculation of effect sizes, regression, time series analysis, and more. Students also get a complete introduction to the open-source R programming language and its key packages. Throughout the text, simple commands in R demonstrate essential data analysis skills using real-data examples. The companion website provides annotated R code for the book's examples, in-class exercises, supplemental reading lists, and links to online videos, interactive materials, and other resources.Pedagogical Features:
Playful, conversational style and gradual approach; suitable for students without strong math backgrounds.
End-of-chapter exercises based on real data supplied in the free R package.
Technical Explanation and Equation/Output boxes.
Appendices on how to install R and work with the sample datasets.Statistical Vocabulary
Reasoning with Probability
Probabilities in the Long Run
Introducing the Logic of Inference
Bayesian and Traditional Hypothesis Testing
Comparing Groups and Analyzing Experiments
Associations between Variables
Linear Multiple Regression
Interactions in ANOVA and Regression
Logistic Regression
Analyzing Change over Time
Dealing with Too Many Variables
All Together Now
Appendixes
Getting Started with R
Working with Data Sets in R
Using dplyr with Data Frames