An Introduction to Statistical Inference and Its Applications with R [Hardcover]

Emphasizing concepts rather than recipes, An Introduction to Statistical Inference and Its Applications with Rprovides 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 samplesnot 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.

Experiments
Examples
Randomization
The Importance of Probability
Games of Chance
Mathematical Preliminaries
Sets
Counting
Functions
Limits
Probability
Interpretations of Probability
Axioms of Probability
Finite Sample Spaces
Conditional Probability
Random Variables
Case Study: Padrolling in Milton Murayamas All I asking for is my body
Discrete Random Variables
Basic Concepts
Examples
Expectation
Binomial Distributions
Continuous Random Variables
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