Introduction to Bayesian Statistics [Illustrated] (Hardcover)
William M. Bolstad (Author)

Editorial Reviews
Review
"I would recommend this book if you are interested in teaching an introductory in Bayesian statistics…"  (The American Statistician, February 2006)
"…a very useful undergraduate text presenting a novel approach to an introductory statistics course." (Biometrics, September 2005)
"I cannot think of a better book for teachers of introductory statistics who want a readable and pedagogically sound text to introduce Bayesian statistics." (Statistics in Medical Research, October 2005)
"…this book fills a gap for teaching elementary Bayesian statistics…it could easily serve as a self-learning text…" (Technometrics, May 2005)
[In a review comparing Bolstad with another book,] "I will keep both of these books on my shelf, but I expect that Bolstad will be the one most borrowed by my colleagues."(significance, December 2004)
"...does an excellent job of presenting Bayesian Statistics as a perfectly reasonable approach to elementary problems of statistics…I must heartily recommend this book…" (STATS: The Magazine for Students of Statistics, Fall 2004)

Product Description
Traditionally, introductory statistics courses have been taught from a frequentist perspective. The recent upsurge in the use of Bayesian methods in applied statistical analysis highlights the need to expose students early on to the Bayes theorem, its advantages, and its applications. Based on the author’s successful courses, Introduction to Bayesian Statistics introduces statistics from a Bayesian perspective in a way that is understandable to readers with a reasonable mathematics background.
Covering most of the same ground found in a typical statistics book--but from a Bayesian perspective--Introduction to Bayesian Statistics offers thorough, clearly-explained discussions of:

· Scientific data gathering, including the use of random sampling methods and randomized experiments to make inferences on cause-effect relationships

· The rules of probability, including joint, marginal, and conditional probability

· Discrete and continuous random variables

· Bayesian inferences for means and proportions compared with the corresponding frequentist ones

· The simple linear regression model analyzed in a Bayesian manner

To assist in the understanding of Bayesian statistics, this introduction provides readers with exercises (with selected answers); summaries of main points from each chapter; a calculus refresher, and a summary on the use of statistical tables; and R functions and Minitab macros for Bayesian analysis and Monte Carlo simulations (downloadable from the associated Web site)

Product Details Hardcover: 376 pages Publisher: Wiley-Interscience; 1 edition (April 26, 2004) Language: English ISBN-10: 0471270202 ISBN-13: 978-0471270201

Contents
Frontmatter (p i-xviii)
Chapter 1: Introduction to Statistical Science (p 1-11)
Chapter 2: Scientific Data Gathering (p 13-28)
Chapter 3: Displaying and Summarizing Data (p 29-54)
Chapter 4: Logic, Probability, and Uncertainty (p 55-74)
Chapter 5: Discrete Random Variables (p 75-94)
Chapter 6: Bayesian Inference for Discrete Random Variables (p 95-109)
Chapter 7: Continuous Random Variables (p 111-127)
Chapter 8: Bayesian Inference for Binomial Proportion (p 129-146)
Chapter 9: Comparing Bayesian and Frequentist Inferences for Proportion (p 147-167)
Chapter 10: Bayesian Inference for Normal Mean (p 169-192)
Chapter 11: Comparing Bayesian and Frequentist Inferences for Mean (p 193-208)
Chapter 12: Bayesian Inference for Difference between Means (p 209-234)
Chapter 13: Bayesian Inference for Simple Linear Regression (p 235-260)
Chapter 14: Robust Bayesian Methods (p 261-274)
Appendix A: Introduction to Calculus (p 275-293)
Appendix B: Use of Statistical Tables (p 295-306)
Appendix C: Using the Included Minitab Macros (p 307-315)
Appendix D: Using the Included R Functions (p 317-327)
Appendix E: Answers to Selected Exercises (p 329-347)
References (p 349-350)
Index (p 351-354)