Statistics Using R
An Integrative Approach

Accessible and engaging introduction to applied statistics using real data integrated with the learning of R.

Sharon Lawner Weinberg (Author), Daphna Harel (Author), Sarah Knapp Abramowitz (Author)

9781009400121, Cambridge University Press

Paperback / softback, published 7 December 2023

726 pages
25.2 x 20 x 4 cm, 1.522 kg

'I loved this book, and I felt that the text had a really excellent method of introducing the topics, describing the R code, and discussing the R output. Equations were also used in a way where the material was easily understood … I found the use of visuals, text, and equations more effective than other textbooks.' John Gallo, Gonzaga University

Statistics Using R introduces the most up-to-date approaches to R programming alongside an introduction to applied statistics using real data in the behavioral, social, and health sciences. It is uniquely focused on the importance of data management as an underlying and key principle of data analysis. It includes an online R tutorial for learning the basics of R, as well as two R files for each chapter, one in Base R code and the other in tidyverse R code, that were used to generate all figures, tables, and analyses for that chapter. These files are intended as models to be adapted and used by readers in conducting their own research. Additional teaching and learning aids include solutions to all end-of-chapter exercises and PowerPoint slides to highlight the important take-aways of each chapter. This textbook is appropriate for both undergraduate and graduate students in social sciences, applied statistics, and research methods.

1. Introduction
2. Examining Univariate Distributions
3. Measures of Location, Spread, and Skewness
4. Re-Expressing Variables
5. Exploring Relationships between Two Variables
6. Simple Linear Regression
7. Probability Fundamentals
8. Theoretical Probability Models
9. The Role of Sampling in Inferential Statistics
10. Inferences Involving the Mean of a Single Population when Σ is Known
11. Inferences Involving the Mean When Σ is Not Known: One- and Two-Sample Designs
12. Research Design: Introduction and Overview
13. One-Way Analysis of Variance
14. Two-Way Analysis of Variance
15. Correlation and Simple Regression as Inferential Techniques
16. An Introduction to Multiple Regression
17. Two-Way Interactions in Multiple Regression
18. Nonparametric Methods
19. Accessing Data from Public Use Sources.

Subject Areas: Psychological methodology [JMB]