Draper N.R., Smith H. Applied Regression Analysis

3rd Edition. — John Wiley & Sons, Inc., 1998. — 736 p. — ISBN: 0-471-17082-8.An outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most widely used statistical tools for discovering the relationships among variables. This classic text, with its emphasis on clear, thorough presentation of concepts and applications, offers a complete, easily accessible introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear and nonlinear regression models, using small and large data sets, with pocket calculators or computers. This Third Edition features separate chapters on multicollinearity, generalized linear models, mixture ingredients, geometry of regression, robust regression, and resampling procedures. Extensive support materials include sets of carefully designed exercises with full or partial solutions and a series of true/false questions with answers. All data sets used in both the text and the exercises can be found on the companion disk at the back of the book. For analysts, researchers, and students in university, industrial, and government courses on regression, this text is an excellent introduction to the subject and an efficient means of learning how to use a valuable analytical tool. It will also prove an invaluable reference resource for applied scientists and statisticians.Basic Prerequisite Knowledge
Fitting a Straight Line by Least Squares
Checking the Straight Line Fit
Fitting Straight Lines: Special Topics
Regression in Matrix Terms: Straight Line Case
The General Regression Situation
Extra Sums of Squares and Tests for Several Parameters Being Zero
Serial Correlation in the Residuals and the Durbin Watson Test
More on Checking Fitted Models
Multiple Regression: Special Topics
Bias in Regression Estimates, and Expected Values of Mean Squares and Sums of Squares
On Worthwhile Regressions, Big F's. and R²
Models Containing Functions of the Predictors, Including Polynomial Models
Transformation of the Response Variable
“Dummy" Variables
Selecting the "Best" Regression Equation
Ill-Conditioning in Regression Data
Ridge Regression
Generalized Linear Models (GLIM)
Mixture Ingredients as Predictor Variables
The Geometry of Least Squares
More Geometry of Least Squares
Orthogonal Polynomials and Summary Data
Multiple Regression Applied to Analysis of Variance Problems
An Introduction to Nonlinear Estimation
Robust Regression
Resampling Procedures (Bootstrapping)True/False Questions
Answers to Exercises
Tables