For more than two decades, the First Edition of Linear Regression Analysis has been an authoritative resource for one of the most common methods of handling statistical data. There have been many advances in the field over the last twenty years, including the development of more efficient and accurate regression computer programs, new ways of fitting regressions, and new methods of model selection and prediction. Linear Regression Analysis, Second Edition, revises and expands this standard text, providing extensive coverage of state–of–the–art theory and applications of linear regression analysis.
Requiring no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight–line regression and simple analysis of variance models, this new edition features:
- Up–to–date accounts of computational methods and algorithms currently in use without getting entrenched in minor computing details
- A careful and detailed survey of the research literature, making this a highly useful reference
- Expanded coverage of diagnostics, and more discussion of methods of model fitting, model selection and prediction
- More than 200 problems throughout the book plus outline solutions
Concise, mathematically clear, and comprehensive, Linear Regression Analysis, Second Edition, serves as both a reliable reference for the practitioner and a valuable textbook for the student.
Vectors of Random Variables.
Multivariate Normal Distribution.
Linear Regression: Estimation and Distribution Theory.
Confidence Intervals and Regions.
Analysis of Variance.
Departures from Underlying Assumptions.
Departures from Assumptions: Diagnosis and Remedies.
Computational Algorithms for Fitting a Regression.
Prediction and Model Selection.
Appendix A. Some Matrix Algebra.
Appendix B. Orthogonal Projections.
Appendix C. Tables.
Outline Solutions to Selected Exercises.