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 tation. Numerous examples, case studies, and exercises are included. R is used to simplify computation, create figures, and draw pseudorandom samples-t 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 diagstic 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.
Michael W. Trosset is Professor of Statistics and Director of the Indiana Statistical Consulting Center at Indiana University.
Michael W. Trosset
Taylor & Francis Inc
Date of Publication
Science & Mathematics: Textbooks & Study Guides
Chapman & Hall/CRC Texts in Statistical Science
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eBay Product ID (ePID)
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Chapman & Hall/CRC
72 black & white illustrations, 30 black & white tables