- Used Books
- Staff Picks
- Gifts & Gift Cards
- Sell Books
- Stores & Events
- Let's Talk Books
Special Offers see all
More at Powell's
Recently Viewed clear list
Ships in 1 to 3 days
available for shipping or prepaid pickup only
Available for In-store Pickup
in 7 to 12 days
Other titles in the Duxbury Applied series:
Introduction to Regression Modeling with CDROM (Duxbury Applied)by Bovas Abraham
Synopses & Reviews
Using a data-driven approach, this book is an exciting blend of theory and interesting regression applications. Students learn the theory behind regression while actively applying it. Working with many case studies, projects, and exercises from areas such as engineering, business, social sciences, and the physical sciences, students discover the purpose of regression and learn how, when, and where regression models work. The book covers the analysis of observational data as well as of data that arise from designed experiments. Special emphasis is given to the difficulties when working with observational data, such as problems arising from multicollinearity and "messy" data situations that violate some of the usual regression assumptions. Throughout the text, students learn regression modeling by solving exercises that emphasize theoretical concepts, by analyzing real data sets, and by working on projects that require them to identify a problem of interest and collect data that are relevant to the problem's solution. The book goes beyond linear regression by covering nonlinear models, regression models with time series errors, and logistic and Poisson regression models.
Book News Annotation:
This textbook describes the linear regression model with a single predictor variable, regression models containing several explanatory variables, nonlinear models, regression models with time series errors, and logistic and Poisson regression models. Students should have already completed a course in statistics and linear algebra. The CD- ROM contains datasets in several formats.
Annotation ©2004 Book News, Inc., Portland, OR (booknews.com)
Looking for an easy-to-understand text to guide you through the tough topic of regression modeling? INTRODUCTION TO REGRESSION MODELING (WITH CD-ROM) offers a blend of theory and regression applications and will give you the practice you need to tackle this subject through exercises, case studies. and projects that have you identify a problem of interest and collect data relevant to the problem's solution. The book goes beyond linear regression by covering nonlinear models, regression models with time series errors, and logistic and Poisson regression models.
About the Author
Bovas Abraham is the former Director of the Institute for Improvement in Quality and Productivity, and is also a professor in the Department of Statistics and Actuarial Science at the University of Waterloo. Bovas received his Ph.D. from the University of Wisconsin, Madison. He has held visiting positions at the University of Wisconsin, the University of Iowa, and the University of Western Australia. He is the author of the book STATISTICAL METHODS FOR FORECASTING (with Johannes Ledolter) published by Wiley in 1983, and the editor of the volume QUALITY IMPROVEMENT THROUGH STATISTICAL METHODS published by Birkhauser in 1998. Johannes Ledolter is the John F. Murray Professor of Management Sciences at the University of Iowa, and a Professor at the Vienna University of Economics and Business Administration. His graduate degrees are in Statistics (M.S. and Ph.D. from the University of Wisconsin, and M.S. from the University of Vienna). He has held visiting positions at Princeton University and Yale University. He is the author of four books: STATISTICAL METHODS FOR FORECASTING (with Bovas Abraham) published by Wiley in 1983, STATISTICS FOR ENGINEERS AND PHYSICAL SCIENTISTS (2nd edition, with Robert V. Hogg) published by Macmillan in 1991, STATISTICAL QUALITY CONTROL (with Claude W. Burrill) published by Wiley in 1999, and ACHIEVING QUALITY THROUGH CONTINUAL IMPROVEMENT (with Claude W. Burrill) published by Wiley in 1999.
Table of Contents
1. Introduction to Regression Models. 2. Simple Linear Regression. 3. A Review of Matrix Algebra and Important Results of Random Vectors. 4. Multiple Linear Regression Model. 5. Specification Issues in Regression Models. 6. Model Checking. 7. Model Selection. 8. Case Studies in Linear Regression. 9. Nonlinear Regression Models. 10. Regression Models for Time Series Situations. 11. Logistic Regression. 12. Generalized Linear Models and Poisson Regression. Brief Answers to Selected Exercises. Statistical Tables. References.
What Our Readers Are Saying
Business » General