# Learning Statistics with R: A tutorial for psychology students and other beginners

(4 reviews)

Danielle Navarro, University of New South Wales

Publisher: Danielle Navarro

Language: English

CC BY-SA

## Reviews

Reviewed by Raphael Mondesir, Associate Professor, Seattle Pacific University on 12/10/21

This text (Learning Statistics with R ~ Ed. 0.6) covers every major topic one would expect to encounter in an introductory statistics course, and then some. It will teach its readers everything from levels of measurement, random variables, and... read more

Reviewed by Zheng Zhou, Ph.D. Candidate, Indiana University - Bloomington on 5/5/21

This books covers the fundamentals in both statistics and R programming. I would suggest add a little touch of Bayesian statistics in the section of the Stats Theory given the broad application of Bayesian inference in psychology. read more

Reviewed by Pete Martini, Assistant Professor, Manchester University on 4/23/21

The book did a very good job of gently working students up to analyses in R. The text was clear and incorporated existing datasets that students (and faculty) could use to engage in hands-on learning. read more

Reviewed by Jessica Salvatore, Associate Professor, Sweet Briar College on 1/10/20

This text, version 0.6, clocks in at over 600 manuscript pages (to date no version has been typeset) -- but the length is worth it to gain great coverage. Navarro covers not only everything you could expect to learn in a two-course sequence of... read more

I. Background

• Chapter 1: Why do we learn statistics?
• Chatper 2: A brief introduction to research design

II. An introduction to R

• Chapter 3: Getting started with R
• Chapter 4: Additional R concepts

III. Working with data

• Chapter 5: Descriptive statistics
• Chapter 6: Drawing graphs
• Chapter 7: Pragmatic matters
• Chapter 8: Basic programming

IV. Statistical theory

• Prelude
• Chapter 9: Introduction to probability
• Chapter 10: Estimating unknown quantities from a sample
• Chapter 11: Hypothesis testing

V. Statistical tools

• Chapter 12: Categorical data analysis
• Chapter 13: Comparing two means
• Chapter 14: Comparing several means (one-way ANOVA)
• Chapter 15: Linear regression
• Chapter 16: Factorial ANOVA

VI. Other topics

• Chapter 17: Bayesian statistics
• Chapter 18: Epilogue
• References

## Ancillary Material

• Danielle Navarro