Statistics with R
A Beginner's Guide
- Robert Stinerock - Baruch College, City University of New York, USA
This award-winning guide makes statistics simple.
Written by an expert with over 30 years of teaching experience, the book provides readers with a solid foundation in applying statistical methods. It moves steadily, using everyday examples to make concepts clear and understandable, from descriptive statistics and probability to hypothesis tests and regression.
This third edition:
- Equips readers with the critical thinking skills to navigate data with competence and confidence in an AI age.
- Incorporates the tidyverse, offering students new to programming a straightforward way of learning R.
- Has been fully revised for conciseness while retaining the author’s warm and approachable style.
Providing optional material for students looking to level up their knowledge and packed with features to support learning, such as practice exercises and datasets, this book is the perfect introduction to statistical methods in the social sciences.
This book is a treasure for both instructors and students. It is written by a master, award-winning teacher with an unparalleled expertise of getting difficult concepts across in a deceptively simple fashion. Written in clear functional English, it both teaches the usual applied statistical methods, as well as provides a gentle introduction to Bayesian methods throughout the book. This is, in essence, more of a new book than just a new edition of an existing one. However, the features that made the first edition so successful have been retained: a student needs only basic algebra to understand the conceptual formulations that are illustrated with hands-on real-life examples that will appeal to students and motivate them to understand the importance of statistics in their daily lives.
Introduction to statistics is a busy field, and Stinerock explains the subject in a careful and friendly manner. The inclusion of Bayesian methods in the second edition is an important contribution — when it is encountered at the beginning of the statistical journey, it allows the reader to appreciate the richness of the Bayesian approach without dealing with the analytical and computational complexities of the subject.
This book is a wonderful primer for learning both statistics and introductory R programming. It is clearly written, provides straightforward explanations of traditional and Bayesian methods, has a lot of supporting material for instructors and students including numerous practice data sets and solved exercises.