Probability and Statistical Inference, Global Edition

1. Probability

• 1.1 Properties of Probability
• 1.2 Methods of Enumeration
• 1.3 Conditional Probability
• 1.4 Independent Events
• 1.5 Bayes' Theorem

2. Discrete Distributions

• 2.1 Random Variables of the Discrete Type
• 2.2 Mathematical Expectation
• 2.3 Special Mathematical Expectations
• 2.4 The Binomial Distribution
• 2.5 The Hypergeometric Distribution
• 2.6 The Negative Binomial Distribution
• 2.7 The Poisson Distribution

3. Continuous Distributions

• 3.1 Random Variables of the Continuous Type
• 3.2 The Exponential, Gamma, and Chi-Square Distributions
• 3.3 The Normal Distribution
• 3.4 Additional Models

4. Bivariate Distributions

• 4.1 Bivariate Distributions of the Discrete Type
• 4.2 The Correlation Coefficient
• 4.3 Conditional Distributions
• 4.4 Bivariate Distributions of the Continuous Type
• 4.5 The Bivariate Normal Distribution

5. Distributions of Functions of Random Variables

• 5.1 Functions of One Random Variable
• 5.2 Transformations of Two Random Variables
• 5.3 Several Independent Random Variables
• 5.4 The Moment-Generating Function Technique
• 5.5 Random Functions Associated with Normal Distributions
• 5.6 The Central Limit Theorem
• 5.7 Approximations for Discrete Distributions
• 5.8 Chebyshev's Inequality and Convergence in Probability
• 5.9 Limiting Moment-Generating Functions

6. Point Estimation

• 6.1 Descriptive Statistics
• 6.2 Exploratory Data Analysis
• 6.3 Order Statistics
• 6.4 Maximum Likelihood and Method of Moments Estimation
• 6.5 A Simple Regression Problem
• 6.6 Asymptotic Distributions of Maximum Likelihood Estimators
• 6.7 Sufficient Statistics
• 6.8 Bayesian Estimation

7. Interval Estimation

About our authors

Robert V. Hogg was Professor Emeritus of Statistics at the University of Iowa since 2001. He got his B.A. in mathematics at the University of Illinois and M.S. and Ph.D. degrees in mathematics, specializing in actuarial sciences and statistics, from the University of Iowa. Known for his gift of humor and his passion for teaching, Hogg had far-reaching influence in the field of statistics. Throughout his career, he played a major role in defining statistics as a unique academic field, and almost literally "wrote the book" on the subject. He authored more than 70 research articles and co-authored 4 books, including Introduction of Mathematical Statistics, 6th Edition with J. W. McKean and A.T. Craig; Applied Statistics for Engineers and Physical Scientists, 3rd Edition with J. Ledolter; and A Brief Course in Mathematical Statistics, 1st Edition with E.A. Tanis. His texts have become classroom standards used by hundreds of thousands of students.

Among the many awards he received for distinction in teaching, Hogg was honored at the national level (the Mathematical Association of America Award for Distinguished Teaching), the state level (the Governor's Science Medal for Teaching), and the university level (Collegiate Teaching Award). His important contributions to statistical research have been acknowledged by his election to fellowship standing in the ASA and the Institute of Mathematical Statistics.

Elliot Tanis, Professor Emeritus of Mathematics at Hope College, received his M.S. and Ph.D. degrees from the University of Iowa. Tanis is the co-author of A Brief Course in Mathematical Statistics with R. Hogg and Probability and Statistics: Explorations with MAPLE, 2nd Edition with Z. Karian. He has authored over 30 publications on statistics and is a past chairman and governor of the Michigan MAA, which