A Second Course in Statistics: Regression Analysis

by William Mendenhall, Terry Sincich

The Second Course in Statistics is an increasingly important offering since more students are arriving at college having taken AP Statistics in high school. Mendenhall/Sincich's A Second Course in Statistics is the perfect book for courses that build on the knowledge students gain in AP Statistics, or the freshman Introductory Statistics course. A Second Course in Statistics: Regression Analysis, Seventh Edition, focuses on building linear statistical models and developing skills for implementing regression analysis in real situations. This text offers applications for engineering, sociology, psychology, science, and business. The authors use real data and scenarios extracted from news articles, journals, and actual consulting problems to show how to apply the concepts. In addition, seven case studies, now located throughout the text after applicable chapters, invite students to focus on specific problems, and are suitable for class discussion.

752 pages, Paperback

First published January 1, 1981

Reviews

[alexis peat]

do not recommend getting a masters degree in statistics

[Matt · Rating 5 out of 5]

Ohh yeah, regress my ass

[fedya]

This book is not really your general “math” textbook. The authors clearly aim to make the material accessible even to readers without a strong mathematical background, which mathematicians or statisticians may find insufficiently rigorous. While the exposition and overall structure are not my favorite, the book does cover a wide and comprehensive range of topics. Importantly, it goes beyond model construction and assumptions to emphasize what regression models actually mean, both conceptually and in practice, and how to exercise sound judgment not only about the model itself but also about the context in which it is applied, the consequences of its underlying assumptions, and the caveats and potential pitfalls that may arise. This interpretive and context-aware emphasis is sometimes forgotten in practice, despite being crucial to the discipline and one of the features that distinguishes statistics from areas where interpretability is often secondary, such as our neighbouring discipline of machine learning.

[Dr. Jake Brock · Rating 1 out of 5]

Disorganized, disjointed and unclear on explanations. Have seen far better statistics books.

[Rusty · Rating 4 out of 5]

This is a textbook used in an undergraduate statistics class. Good book.

[Khoa · Rating 3 out of 5]

I had to read this my class, so it's okay. Could've had more explanations.