Probability and Statistical Inference: United States Edition

This applied introduction to the mathematics of probability and statistics e mphasizes the existence of variation in almost every process, and how the study of probability and statistics helps us understand this variability. Designed for students with a background in calculus, it reinforces basic mathematical concepts with numerous real-world examples and applications to illustrate the relevance of key concepts.

Table of Contents:
Table of Contents   Chapter 1: Probability Basic Concepts Properties of Probability Methods of Enumeration Conditional Probability Independent Events Bayes' Theorem   Chapter 2: Discrete Distributions Random Variables of the Discrete Type Mathematical Expectation Bernoulli Trials and the Binomial Distribution The Moment-Generating Function The Poisson Distribution   Chapter 3: Continuous Distributions Continuous-Type Data and EDA Random Variables of the Continuous Type The Uniform and Exponential Distributions The Gamma and Chi-Square Distributions Distributions of Functions of a Random Variable Additional Models   Chapter 4: Multivariate Distributions Distributions of Two Random Variables The Correlation Coefficient Conditional Distributions Transformations of Random Variables Independent Random Variables Distributions of Sums of Independent Random Variables Chebyshev's Inequality and Convergence in Probability   Chapter 5: The Normal Distribution A Brief History of Probability The Normal Distribution Random Functions Associated with Normal Distributions The Central Limit Theorem Approximations for Discrete Distributions The Bivariate Normal Distribution Limiting Moment-Generating Functions Importance of Understanding Variability   Chapter 6: Estimation Sample Characteristics Point Estimation Sufficient Statistics Confidence Intervals for Means Confidence Intervals for Difference of Two Means Confidence Intervals for Variances Confidence Intervals for Proportions Sample Size Order Statistics Distribution-Free Confidence Intervals for Percentiles A Simple Regression Problem More Regression Resampling Methods Asymptotic Distributions of Maximum Likelihood Estimators   Chapter 7: Bayesian Methods Subjective Probability Bayesian Estimation More Bayesian Concepts   Chapter 8: Tests of Statistical Hypotheses Tests About Proportions Tests About One Mean and One Variance Tests of the Equality of Two Normal Distributions The Wilcoxon Tests Chi-Square Goodness of Fit Tests Contingency Tables One-Factor Analysis of Variance Two-Factor Analysis of Variance Tests Concerning Regression and Correlation Kolmogorov-Smirnov Goodness of Fit Test Run Test and Test for Randomness   Chapter 9: Theory of Statistical Inference Power of a Statistical Test Best Critical Regions Likelihood Ratio Tests   Chapter 10: Quality Improvement Through Statistical Methods Time Sequences Statistical Quality Control General Factorial and 2 k Factorial Designs More on Design of Experiments Epilogue A Review of Selected Mathematical Techniques Algebra of Sets Mathematical Tools for the Hypergeometric Distribution Limits Infinite Series Integration Multivariate Calculus

Review :
"Generally, I think the pedagogy is excellent, providing an almost holistic introduction to statistics, both its mathematical and applications sides. Elements of the subject are introduced in increasing layers of complexity, at a rate that is challenging yet measured. Masterfully done. My overall impression of the book is quite favorable and actually I am considering this text for my next cycle of classes. Strengths: excellent use of examples to illustrate concepts; strong exercise selections; the discussions are generally clear, animated, and focused with the main drive of the text in mind. All the necessary topics central to modern statistics are introduced. The level of the text is quite good for an undergraduate introductory course. Many rather difficult ideas are presented simply, but effectively enough to prepare students for later topics and courses. And the text really bares the soul of statistics. The text motivates the theory by keeping it connected to real-world applications." — David F. Snyder, Texas State University "Probability and Statistical Inference is a great text to use for a one-year course, where the students are just becoming mathematically prepared. The authors write with great care and clearly develop and motivate the subject. This edition also contains a chapter on Bayesian methods. Chapter 7 is an interesting and modern treatment of the subject—a subject that has included some controversy. Although I am a probabilist, I am certainly pleased to see this treatment in an undergraduate text. Bayesian methods have long needed suitable treatment at the undergraduate level. It provides an up-to-date and complete treatment of mathematics of probability and statistics. This edition also includes many new examples, applications, and exercises. Each of these has improved in an already outstanding text." — Randall Swift, California State Polytechnic University, Pomona "This latest version of Hogg and Tanis contains many more realistic data scenarios that rely much less on coin tossing and dice and card examples. Students will likely better understand the material if they can relate to the examples. The authors strike a good balance between readability and rigor. The material is accurately presented and theorems are accurate. I am glad to see section 6.13 'Resampling Methods' induded. The 'bootstrap' has been around now for 20 years but many mathematical statistics books still neglect it or relegate it to the exercises. This is a good indication that the text is up-to-date." — Paul Joyce, University of Idaho "This is a good, solid, calculus-based introduction to probability and statistics at the sophomore-junior level. I have used this textbook twice for such a course, and would like to use it again in the future." — Ching-Yuan Chiang, James Madison University "The examples in the book are very clear and easy to follow. My students would benefit from this book more than our current textbook." — Mark Ghamsary, Loma Linda University