Applied Linear Regression

by Sanford Weisberg

Praise for the Third Edition

"...this is an excellent book which could easily be used as a course text..." —International Statistical Institute

The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples.

Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illustrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. While maintaining the accessible appeal of each previous edition,Applied Linear Regression, Fourth Edition 

Graphical methods stressed in the initial exploratory phase, analysis phase, and summarization phase of an analysis In-depth coverage of parameter estimates in both simple and complex models, transformations, and regression diagnostics Newly added material on topics including testing, ANOVA, and variance assumptions Updated methodology, such as bootstrapping, cross-validation binomial and Poisson regression, and modern model selection methods Applied Linear Regression, Fourth Edition is an excellent textbook for upper-undergraduate and graduate-level students, as well as an appropriate reference guide for practitioners and applied statisticians in engineering, business administration, economics, and the social sciences.

Genres: Mathematics, Reference, Textbooks

371 pages, Kindle Edition

First published January 1, 1980

Reviews

[Shelby Gonzalez · Rating 3 out of 5]

Read for my stats class on linear regression. Definitely hard to stay awake while reading sometimes

[RA · Rating 4 out of 5]

I read the fourth edition and was happy to learn a number of things that were new to me. Not everything was easy to understand or complete, but I was not disappointed, it is a book worth reading, although as for most other statistics books, one cannot expect to cover everything.

[Shika · Rating 3 out of 5]

Assumes you have linear algebra/strong mathematical background. If you're a biologistic looking to starting in Biostatistics, would recommend looking elsewhere. If you're a statistician looking for applied linear regression basics, then this would be better suited for you.

[Goo · Rating 2 out of 5]

I read some of this as a secondary text when first learning the theory of linear normal models. My primary text lacked certain aspects of actually applying linear models which this text covered in a simple fashion. However, the text is not suited as an introduction to the mathematical theory behind linear models.

[Patrick · Rating 2 out of 5]

I read this as a graduate student in statistics, there are many better books.