An Introduction to Statistical Inference and Its Applications with R

An Introduction to Statistical Inference and Its Applications with R is a textbook that provides a clear exposition of the methods of statistical inference for students who are comfortable with mathematical notation. It covers point estimation, hypothesis testing, confidence intervals, 1- and 2-sample location problems, analysis of variance, goodness-of-fit, correlation and regression, and the role of simulation in modern statistical inference.

Format: Paperback / softback
Length: 496 pages
Publication date: 21 January 2023
Publisher: Taylor & Francis Ltd

An Introduction to Statistical Inference and Its Applications with R is a comprehensive guide that emphasizes concepts rather than recipes, making it suitable for students who are comfortable with mathematical notation. The book provides a clear exposition of the methods of statistical inference, covering topics such as chance in experimentation, basic tools of probability, the plug-in principle, point estimation, hypothesis testing, confidence intervals, 1- and 2-sample location problems, analysis of variance, goodness-of-fit, correlation and regression, and the role of simulation in modern statistical inference.

Numerous examples, case studies, and exercises are included to reinforce the concepts and help students apply the knowledge gained. R is used throughout the book to simplify computation, create figures, and draw pseudorandom samples, rather than being used to perform entire analyses.

The text begins by discussing the importance of chance in experimentation and then develops basic tools of probability. The plug-in principle is introduced to bridge the gap between populations and samples, motivating various summary statistics and diagnostic techniques.

The heart of the text is a careful exposition of point estimation, hypothesis testing, and confidence intervals. The author then explains procedures for 1- and 2-sample location problems, analysis of variance, goodness-of-fit, and correlation and regression. He concludes by discussing the role of simulation in modern statistical inference.

Focusing on the assumptions that underlie popular statistical methods, this textbook explains how and why these methods are used to analyze experimental data. It provides a solid foundation for students who want to learn statistical inference and its applications in a practical and accessible manner.

Overall, An Introduction to Statistical Inference and Its Applications with R is an invaluable resource for students, researchers, and practitioners in the fields of statistics, data analysis, and applied sciences.

Weight: 920g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032477725