Statistics with R: A Beginner's Guide

This book provides a clear and practical guide to using R and applied statistical methods,with step-by-step instructions,real-world examples,hands-on exercises,and online resources. It is ideal for anyone looking to gain a basic competence in statistics,prepare for advanced courses,or interpret research from across the social sciences.

Format: Paperback / softback
Length: 448 pages
Publication date: 21 November 2022
Publisher: SAGE Publications Ltd

This comprehensive guide to using R and applied statistical methods is an invaluable resource for anyone seeking to simplify the complexities of statistics. Written by an experienced instructor with over 30 years of teaching experience, the book offers a clear and step-by-step approach to mastering statistical techniques. Real-world examples and hands-on exercises help readers apply their knowledge to real-world scenarios, while online resources such as R scripts, datasets, screencasts, and multiple-choice questions provide additional support and practice. Whether you are a complete beginner or looking to advance your statistical skills, this book is an essential tool for anyone working in the social sciences.

The book is divided into four main sections:

Introduction to Statistics: This section provides a comprehensive overview of statistical concepts, including probability, hypothesis testing, and data analysis. It covers basic statistical models, such as linear regression and logistic regression, and introduces readers to the R programming language.

Data Analysis with R: This section teaches readers how to import, clean, and analyze data using R. It covers data manipulation, visualization, and statistical analysis techniques, such as t-tests, ANOVA, and regression analysis. Readers will learn how to create informative plots and tables to present their findings.

Bayesian Methods: This section introduces readers to Bayesian methods, which are an alternative approach to statistical inference. It covers the basics of Bayesian inference, including prior distributions, posterior distributions, and Markov chain Monte Carlo (MCMC) methods. Readers will learn how to implement Bayesian models and perform inference using R.

Applications in the Social Sciences: This section showcases how R and applied statistical methods can be used in various social sciences fields. It includes examples from psychology, sociology, economics, and political science, demonstrating how statistical techniques can be used to answer research questions and interpret data.

Throughout the book, the author emphasizes the importance of statistical literacy and provides readers with the practical skills they need to apply statistical methods in their research. Real-world examples and hands-on exercises help readers connect theoretical concepts to practical applications, while online resources provide additional support and practice.

In addition to the main text, the book includes a wealth of online resources:

Over 90 R scripts: These scripts cover a wide range of statistical techniques and applications, from basic data analysis to more advanced Bayesian methods.
36 datasets: These datasets are used throughout the book to demonstrate statistical concepts and provide readers with real-world data to analyze.
37 screencasts: These screencasts accompany the R scripts and provide step-by-step instructions on how to implement statistical techniques using R.
Complete solutions for all exercises: These solutions provide detailed explanations and code for all exercises in the book, allowing readers to practice their skills and reinforce their understanding.
130 multiple-choice questions: These questions test readers' knowledge and understanding of the material covered in the book.

Whether you are a student, researcher, or professional working in the social sciences, this comprehensive guide to using R and applied statistical methods is an invaluable resource. It provides a clear and accessible introduction to statistical concepts and techniques, while also offering practical guidance and support for applying these methods in real-world scenarios. With its wealth of online resources and comprehensive coverage, this book is an essential tool for anyone seeking to simplify the complexities of statistics and gain a deeper understanding of data analysis.

Weight: 748g
Dimension: 170 x 242 x 29 (mm)
ISBN-13: 9781529753523
Edition number: 2 Revised edition