Statistical Reasoning for Everyday Life [With CDROM]

by Jeffrey O. Bennett, Mario F. Triola, William L. Briggs, et al.

Book Overview

ALERT: Before you purchase, check with your instructor or review your course syllabus to ensure that you select the correct ISBN. Several versions of Pearson's MyLab & Mastering products exist for... This description may be from another edition of this product.

• Format: Paperback
• Language: English
• ISBN: 0321286723
• ISBN13: 9780321286727
• Release Date: January 2008
• Publisher: Addison Wesley Longman
• Length: 458 Pages
• Weight: 2.20 lbs.
• Dimensions: 0.7" x 8.7" x 10.0"

Customer Reviews

Easy to understand

This textbook was a breath of fresh air when compared with other statistics textbooks required for university level classes. I found this text to be very easy to understand, and the examples used were very helpful.

Delivers On Its Promises

This textbook is well-conceived and well-executed. The authors have a strong background in science as well as a keen interest in making technical subjects accessible to a broad audience. They have succeeded in presenting statistics with a minimum of mathematics. Emphasis is placed upon the application of these concepts to real life situations taken from news sources, the internet and individual experiences. The graphics, sidebars, focus sections and case studies are timely, relevant, engaging, lucidly written and enlightening. A one-page epilog provides a nice summary statement to the entire textbook. A glossary, index, answer key , appendices and suggestions for further reading are also included. This is an excellent textbook for a general education student whose career would not require the direct use of statistical tools. None of the usual tables are discussed or present in this textbook, but all of the main topics are treated in a thorough and thought-provoking fashion. The authors' discussion of correlation and causality is especially good, although their definition of causality in the glossary as " the relationship present when one variable is a cause of another " is circular. Instead, the authors should reiterate their assertion from the main text that a "cause" is a "physical mechanism" based upon a "physical model" which is generally acceptable as a scientific explanation.