and tolerance regions. Subsequent chapters provide in-depth coverage of key topics including: * Univariate normal distribution * Non-normal distributions * Univariate linear regression models * Nonparametric tolerance intervals * The one-way random model with balanced data * The multivariate normal distribution * The one-way random model with unbalanced data * The multivariate linear regression model * General mixed models * Bayesian tolerance intervals A final chapter contains coverage of miscellaneous topics including tolerance limits for a ratio of normal random variables, sample size determination, reference limits and coverage intervals, tolerance intervals for binomial and Poisson distributions, and tolerance intervals based on censored samples. Theoretical explanations are accompanied by computational algorithms that can be easily replicated by readers, and each chapter contains exercise sets for reinforcement of the presented material. Detailed appendices provide additional data sets and extensive tables of univariate and multivariate tolerance factors. Statistical Tolerance Regions is an ideal book for courses on tolerance intervals at the graduate level. It is also a valuable reference and resource for applied statisticians, researchers, and practitioners in industry and pharmaceutical companies.