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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.
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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.Review:
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.
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