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An Introduction to Statistical Inference and Its Applications with R: 81 (Chapman & Hall/CRC Texts in Statistical Science) - Hardcover
Synopsis
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" may belong to another edition of this title.
About the Author
Michael W. Trosset is Professor of Statistics and Director of the Indiana Statistical Consulting Center at Indiana University.
"About this title" may belong to another edition of this title.
- PublisherChapman and Hall/CRC
- Publication date2011
- ISBN 10 1584889470
- ISBN 13 9781584889472
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages496
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An Introduction to Statistical Inference and Its Applications with R (Chapman & Hall/CRC Texts in Statistical Science)
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An Introduction to Statistical Inference and Its Applications with R (Hardcover)
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Hardcover. Condition: new. Hardcover. 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. Explains how statistical methods are used for data analysis and uses the elementary functions of R to perform the individual steps of statistical procedures. This title introduces basic concepts of inference through a study of several important procedures, including parametric and nonparametric methods, analysis of variance, and regression. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781584889472
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