Designed as an introduction to statistical distribution theory. Includes a first chapter on basic notations and definitions that are essential to working with distributions. Remaining chapters are divided into three parts: Discrete Distributions, Continuous Distributions, and Multivariate Distributions.

Preface. Preliminaries. I. DISCRETE DISTRIBUTIONS. Discrete Uniform Distribution. Degenerate Distribution. Bernoulli Distribution. Binomial Distribution. Geometric Distribution. Negative Binomial Distribution. Hypergeometric Distribution. Poisson Distribution. Miscellanea. II. CONTINUOUS DISTRIBUTIONS. Uniform Distribution. Cauchy Distribution. Triangular Distribution. Power Distribution. Pareto Distribution. Beta Distribution. Arcsine Distribution. Exponential Distribution. Laplace Distribution. Gamma Distribution. Extreme Value Distributions. Logistic Distribution. Normal Distribution. Miscellanea. III. MULTIVARIATE DISTRIBUTIONS. Multinomial Distribution. Multivariate Normal Distribution. Dirichlet Distribution. Appendix -  Pioneers in Distribution Theory. Bibliography. Author Index. Subject Index.

Introducing the perfect, all-in-one primer on statistical distributions Statistical distributions, along with their properties and interrelationships, are a central part of advanced statistics. However, most statistics textbooks only devote a few chapters to basic statistical distributions–such as binomial, Poisson, exponential, and normal–and stop short of covering other important distributions geared toward upper-level statistics courses. That’s where A Primer on Statistical Distributions makes its mark. Specifically tailored to the introductory course on statistical distributions, this unmatched resource takes a more balanced, all-inclusive approach than similar texts. In page after page, you’ll find a valuable review of often-overlooked distributions, including geometric, negative binomial, hypergeometric, Pareto, beta, gamma, chi-square, logistic, Cauchy, Laplace, extreme value, multinomial, Dirichlet, and multivariate normal. A Primer on Statistical Distributions begins with an informative first chapter on preliminary notations, definitions, and the concepts that are necessary to work effectively with distributions. The basic topics covered in this introductory chapter include distribution types, generating functions, shape characteristics, entropy, random vectors, conditional distributions, and regressions. Subsequent chapters are divided into three parts: discrete distributions, continuous distributions, and multivariate distributions. Each chapter includes many skill-building exercises that provide a helpful review of the material just discussed. And the book also contains an appendix with engaging biographical sketches of some of the leading minds behind the development of statistical distributions theory. A Primer on Statistical Distributions is not only ideal for students and professionals in statistics, it can also benefit individuals in applied areas such as psychology, geography, economics, and engineering, and even professionals in need of a logically organized, comprehensive reference to statistical distributions. It all adds up to a text that no one utilizing statistical distributions should be without.