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From the preface: This book is intended as a text for a basic course in linear regression analysis. It contains the standard topics as well as some of the newer and more unconventional ones, and blends both theory and application so that the reader will obtain an understanding of the basic principles necessary to apply regression methods in a variety of practical settings.
It is assumed that the reader has a basic knowledge of statistics such as that usually obtained from a first course, including familiarity with significance tests, confidence intervals, and the normal, t, , and F distributions. Some knowledge of matrix algebra is also necessary.
Chapter headings: 1. Introduction; 2. Simple linear regression and correlation; 3. Measures of model adequacy; 4. Multiple linear regression; 5. Polynomial regression models; 6. Indicator variables; 7. Variable selection and model building; 8. Multicollinearity; 9. Topics in the use of regression analysis; 10. Validation of regression models.