Reasoning with Data

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.

Table of Contents

Chapter 1. Statistical Vocabulary
Chapter 2. Reasoning With Probability
Chapter 3. Probabilities In The Long Run
Chapter 4. Introducing The Logic Of Inference Using Confidence Intervals
Chapter 5. Bayesian And Traditional Hypothesis Testing
Chapter 6. Comparing Groups And Analyzing Experiments
Chapter 7. Associations Between Variables
Chapter 8. Linear Multiple Regression
Chapter 9. Interactions In Anova And Regression
Chapter 10. Logistic Regression
Chapter 11. Analyzing Change Over Time
Chapter 12. Dealing With Too Many Variables
Chapter 13. All Together Now
Appendix A. Getting Started with R
Appendix B. Working with Data Sets in R
Appendix C. Using dplyr with Data Frames