For one- or two-semester courses in Probability, Probability &amp; Statistics, or Mathematical Statistics. An authoritative introduction to an in-demand field Advances in computing technology – particularly in science and business – have increased the need for more statistical scientists to examine the huge amount of data being collected. Written by veteran statisticians, Probability and Statistical Inference, 10th Edition emphasizes the existence of variation in almost every process, and how the study of probability and statistics helps us understand this variation. This applied introduction to probability and statistics reinforces basic mathematical concepts with numerous real-world examples and applications to illustrate the relevance of key concepts. It is designed for a two-semester course, but it can be adapted for a one-semester course. A good calculus background is needed, but no previous study of probability or statistics is required. 013518939X / 9780135189399 PROBABILITY AND STATISTICAL INFERENCE, 10/e

Probability and Statistical Inference

Science & Math

Hogg, Robert

Tanis, Elliot

Zimmerman, Dale

Mathematics

Pearson

Written by three veteran statisticians, this applied introduction to probability and statistics emphasizes the existence of variation in almost every process, and how the study of probability and statistics helps us understand this variation. Designed for students with a background in calculus, this book continues to reinforce basic mathematical concepts with numerous real-world examples and applications to illustrate the relevance of key concepts.

See the best price to sell, buy, or rent books by Hogg, Robert. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Hogg, Robert

See the best price to sell, buy, or rent books by Hogg, Robert. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Probability and Statistical Inference

Probability and Statistical Inference