Concise, mathematically clear, and comprehensive treatment of the subject.
* Expanded coverage of diagnostics and methods of model fitting.
* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.
* More than 200 problems throughout the book plus outline solutions for the exercises.
* This revision has been extensively class-tested.Content:
Chapter 1 Vectors of Random Variables (pages 1–16):
Chapter 2 Multivariate Normal Distribution (pages 17–33):
Chapter 3 Linear Regression: Estimation and Distribution Theory (pages 35–95):
Chapter 4 Hypothesis Testing (pages 97–118):
Chapter 5 Confidence Intervals and Regions (pages 119–137):
Chapter 6 Straight?Line Regression (pages 139–163):
Chapter 7 Polynomial Regression (pages 165–185):
Chapter 8 Analysis of Variance (pages 187–226):
Chapter 9 Departures from Underlying Assumptions (pages 227–263):
Chapter 10 Departures from Assumptions: Diagnosis and Remedies (pages 265–328):
Chapter 11 Computational Algorithms for Fitting a Regression (pages 329–389):
Chapter 12 Prediction and Model Selection (pages 391–456):
