Probability with STEM Applications

Probability with STEM Applications, Third Edition, is an accessible and well-balanced introduction to post-calculus applied probability. Integrating foundational mathematical theory and the application of probability in the real world, this leading textbook engages students with unique problem scenarios and more than 1100 exercises of varying levels of difficulty. The text uses a hands-on, software-oriented approach to the subject of probability. MATLAB and R examples and exercises — complemented by computer code that enables students to create their own simulations — demonstrate the importance of software to solve problems that cannot be obtained analytically.

Revised and updated throughout, the textbook covers basic properties of probability, random variables and their probability distributions, a brief introduction to statistical inference, Markov chains, stochastic processes, and signal processing. This new edition is the perfect text for a one-semester course and contains enough additional material for an entire academic year. The blending of theory and application will appeal not only to mathematics and statistics majors but also to engineering students, and quantitative business and social science majors.

New to this Edition:

• Offered as a traditional textbook and in enhanced ePub format, containing problems with show/hide solutions and interactive applets and illustrations
• Revised and expanded chapters on conditional probability and independence, families of continuous distributions, and Markov chains
• New problems and updated problem sets throughout

Features:

• Introduces basic theoretical knowledge in the first seven chapters, serving as a self-contained textbook of roughly 650 problems
• Provides numerous up-to-date examples and problems in R and MATLAB
• Discusses examples from recent journal articles, classic problems, and various practical applications
• Includes a chapter specifically designed for electrical and computer engineers, suitable for a one-term class on random signals and noise
• Contains appendices of statistical tables, background mathematics, and important probability distributions