Probability And Statistical Inference 10th Edition By Hogg Tanis And Zimmerman

Probability and Statistics with Applications: A Problem Solving Text

This text is listed on the Course of Reading for SOA Exam P. Probability and Statistics with Applications is an introductory textbook designed to make the subject accessible to college freshmen and sophomores concurrent with Calc II and III, with a prerequisite of just one smester of calculus. It is organized specifically to meet the needs of students who are preparing for the Society of Actuaries qualifying Examination P and Casualty Actuarial Society's new Exam S. Sample actuarial exam problems are integrated throughout the text along with an abundance of illustrative examples and 870 exercises. The book provides the content to serve as the primary text for a standard two-semester advanced undergraduate course in mathematical probability and statistics. 2nd Edition Highlights Expansion of statistics portion to cover CAS ST and all of the statistics portion of CAS SAbundance of examples and sample exam problems for both Exams SOA P and CAS SCombines best attributes of a solid text and an actuarial exam study manual in one volumeWidely used by college freshmen and sophomores to pass SOA Exam P early in their college careersMay be used concurrently with calculus coursesNew or rewritten sections cover topics such as discrete and continuous mixture distributions, non-homogeneous Poisson processes, conjugate pairs in Bayesian estimation, statistical sufficiency, non-parametric statistics, and other topics also relevant to SOA Exam C.

A Brief Course in Mathematical Statistics

Linear Model Theory

This textbook presents a unified and rigorous approach to best linear unbiased estimation and prediction of parameters and random quantities in linear models, as well as other theory upon which much of the statistical methodology associated with linear models is based. The single most unique feature of the book is that each major concept or result is illustrated with one or more concrete examples or special cases. Commonly used methodologies based on the theory are presented in methodological interludes scattered throughout the book, along with a wealth of exercises that will benefit students and instructors alike. Generalized inverses are used throughout, so that the model matrix and various other matrices are not required to have full rank. Considerably more emphasis is given to estimability, partitioned analyses of variance, constrained least squares, effects of model misspecification, and most especially prediction than in many other textbooks on linear models. This book is intended for master and PhD students with a basic grasp of statistical theory, matrix algebra and applied regression analysis, and for instructors of linear models courses. Solutions to the book’s exercises are available in the companion volume Linear Model Theory - Exercises and Solutions by the same author.