An Introduction to Statistical Inference and Its Applications with R

An Introduction to Statistical Inference and Its Applications with R

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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 more

Product details

  • Hardback | 496 pages
  • 162 x 234 x 32mm | 898.11g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • UK ed.
  • 72 black & white illustrations, 30 black & white tables
  • 1584889470
  • 9781584889472
  • 1,387,261

About Michael W. Trosset

Michael W. Trosset is Professor of Statistics and Director of the Indiana Statistical Consulting Center at Indiana more

Table of contents

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 Murayama's All I asking for is my body Discrete Random Variables Basic Concepts Examples Expectation Binomial Distributions Continuous Random Variables A Motivating Example Basic Concepts Elementary Examples Normal Distributions Normal Sampling Distributions Quantifying Population Attributes Symmetry Quantiles The Method of Least Squares Data The Plug-In Principle Plug-In Estimates of Mean and Variance Plug-In Estimates of Quantiles Kernel Density Estimates Case Study: Are Forearm Lengths Normally Distributed? Transformations Lots of Data Averaging Decreases Variation The Weak Law of Large Numbers The Central Limit Theorem Inference A Motivating Example Point Estimation Heuristics of Hypothesis Testing Testing Hypotheses about a Population Mean Set Estimation 1-Sample Location Problems The Normal 1-Sample Location Problem The General 1-Sample Location Problem The Symmetric 1-Sample Location Problem Case Study: Deficit Unawareness in Alzheimer's Disease 2-Sample Location Problems The Normal 2-Sample Location Problem The Case of a General Shift Family Case Study: Etruscan versus Italian Head Breadth The Analysis of Variance The Fundamental Null Hypothesis Testing the Fundamental Null Hypothesis Planned Comparisons Post Hoc Comparisons Case Study: Treatments of Anorexia Goodness-of-Fit Partitions Test Statistics Testing Independence Association Bivariate Distributions Normal Random Variables Monotonic Association Explaining Association Case Study: Anorexia Treatments Revisited Simple Linear Regression The Regression Line The Method of Least Squares Computation The Simple Linear Regression Model Assessing Linearity Case Study: Are Thick Books More Valuable? Simulation-Based Inference Termite Foraging Revisited The Bootstrap Case Study: Adventure Racing R: A Statistical Programming Language Introduction Using R Functions That Accompany This Book Index Exercises appear at the end of each more

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