<p>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.</p> <p></p> <p>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.</p> <p></p> <p>Focusing on the assumptions that underlie popular statistical methods, this textbook explains how and why these methods are used to analyze experimental data. </p>

Details

• Author: Michael W. Trosset
• Publisher: Taylor & Francis
• Published: 2009-06-23
• Edition: 1
• Pages: 496
• Format: Hardcover
• Language: en

ISBN: 9781584889472