Buch, Englisch, 496 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 1060 g
Buch, Englisch, 496 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 1060 g
Reihe: Chapman & Hall/CRC Texts in Statistical Science
ISBN: 978-1-58488-947-2
Verlag: Chapman and Hall/CRC
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.
Zielgruppe
Undergraduate
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Experiments. Mathematical Preliminaries. Probability. Discrete Random Variables. Continuous Random Variables. Quantifying Population Attributes. Data. Lots of Data. Inference. 1-Sample Location Problems. 2-Sample Location Problems. The Analysis of Variance. Goodness-of-Fit. Association. Simple Linear Regression. Simulation-Based Inference. R: A Statistical Programming Language. Index.
