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This textbook, now in its third edition, offers a practical introduction to probability with statistical applications, covering material for both a first and second undergraduate probability course. The author focuses on essential concepts that every student should thoroughly understand. The content is organized into brief, easy-to-follow chapters, motivated by plenty of examples.
The first part of the book focuses on classical discrete probability distributions, then goes on to study continuous distributions, confidence intervals, and statistical tests. The following section introduces more advanced concepts suitable for a second course in probability, such as random vectors and sums of random variables. The last part of the book is dedicated to mathematical statistics concepts such as estimation, sufficiency, Bayes' estimation, and multiple regression. This third edition includes a new chapter on combinatorics and a more distinct separation betweendiscrete and continuous distributions. Some of the longer chapters in the previous editions have been divided into shorter chapters to allow for more flexible teaching.
Probability with Statistical Applications, Third Edition is intended for undergraduate students taking a first course in probability; later chapters are also suited for a second course in probability and mathematical statistics. Calculus is the only prerequisite; prior knowledge of probability is not required.
List of contents
Probability Space.- Conditional probabilities.- Discrete random variables.- Binomial random variables.- Poisson random variables.- Simulations of discrete random variables.- Combinatorics.- Continuous random variables.- The sample average and sample.- Estimating and testing proportions.- Estimating and testing means.- Small samples.- Chi-squared tests.- Design of experiments.- The cumulative distribution function.- Continuous joint distributions.- Covariance and independence.- Conditional distribution and expectation.- The bivariate normal distribution.- Sums of Bernoulli random variables.- Coupling random variables.- The moment generating function.- The chi-squared, Student and F distributions.- Sampling from a normal distribution.- Finding estimators.- Comparing estimators.- Best unbiased estimators.- Bayes' estimator.- Multiple linear regression.- List of common discrete distributions.- List of common continuous distributions.- Further reading.- Normal table.- Student table.- Chi-squared table.- Index.
About the author
Rinaldo Schinazi is a professor at the University of Colorado Springs. He teaches a wide range of probability and statistics courses. His research is in probability models for population biology.
