Synopses & Reviews
Emphasizing concepts rather than recipes, An Introduction to Statistical Inference and Its Applications with R provides a clear exposition of the methods of statistical inference for students who are comfortable with mathematical notation. Numerous examples, case studies, and exercises are included. R is used to simplify computation, create figures, and draw pseudorandom samples not to perform entire analyses.
After discussing the importance of chance in experimentation, the text develops basic tools of probability. The plug-in principle then provides a transition from populations to samples, motivating a variety of 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.
Synopsis
With an emphasis on explaining how and why statistical methods are used to analyze data, An Introduction to Statistical Inference and its Applications with R introduces several important procedures: one and two sample location problems, one way analysis of variance, and simple linear regression. The book presents numerous applications and supporting datasets throughout along with historical background to illustrate the material. Offering a modern approach that focuses on the interpretation of data, it features an appendix that provides instruction on the use of R as well as datasets and simulation. In addition, R code is available for download on the web.
Synopsis
This text explains how statistical methods are used for data analysis and uses the elementary functions of R to perform the individual steps of statistical procedures. It includes amusing anecdotes and trivia, such as Ambrose Bierce's definition of insurance. The text introduces basic concepts of inference through a careful study of several important procedures, including parametric and nonparametric methods, analysis of variance, and regression. It also presents many applications, supporting data sets, and end-of-chapter exercises. The R code and data sets are available for download online and a solutions manual is available for qualifying instructors.