Applied linear regression - Weisberg, Sanford (Hoboken, New Jersey : John Wiley & Son, Inc., 2014)
"Providing a coherent set of basic methodology for applied linear regression without being encyclopedic, the fourth edition of Applied Linear Regression is thoroughly updated to help students master the theory and applications of linear regression modeling. Focusing on model building, assessing fit and reliability, and drawing conclusions, the text demonstrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. To facilitate quick learning, this updated edition stresses the use of graphical methods in an effort to find appropriate models and to better understand them"-- Provided by publisher.
2.7 The Coefficient of Determination, R22.8 The Residuals; CHAPTER 3: Multiple Regression; 3.1 Adding a Regressor to a Simple Linear Regression Model; 3.2 The Multiple Linear Regression Model; 3.3 Predictors and Regressors; 3.4 Ordinary Least Squares; 3.5 Predictions, Fitted Values, and Linear Combinations; CHAPTER 4: Interpretation of Main Effects; 4.1 Understanding Parameter Estimates; 4.2 Dropping Regressors; 4.3 Experimentation versus Observation; 4.4 Sampling from a Normal Population; 4.5 More on R2; CHAPTER 5: Complex Regressors; 5.1 Factors; 5.2 Many Factors; 5.3 Polynomial Regression 5.4 Splines5.5 Principal Components; 5.6 Missing Data; CHAPTER 6: Testing and Analysis of Variance; 6.1 F-Tests; 6.2 The Analysis of Variance; 6.3 Comparisons of Means; 6.4 Power and Non-Null Distributions; 6.5 Wald Tests; 6.6 Interpreting Tests; CHAPTER 7: Variances; 7.1 Weighted Least Squares; 7.2 Misspecified Variances; 7.3 General Correlation Structures; 7.4 Mixed Models; 7.5 Variance Stabilizing Transformations; 7.6 The Delta Method; 7.7 The Bootstrap; CHAPTER 8: Transformations; 8.1 Transformation Basics; 8.2 A General Approach to Transformations; 8.3 Transforming the Response 8.4 Transformations of Nonpositive Variables8.5 Additive Models; CHAPTER 9: Regression Diagnostics; 9.1 The Residuals; 9.2 Testing for Curvature; 9.3 Nonconstant Variance; 9.4 Outliers; 9.5 Influence of Cases; 9.6 Normality Assumption; CHAPTER 10: Variable Selection; 10.1 Variable Selection and Parameter Assessment; 10.2 Variable Selection for Discovery; 10.3 Model Selection for Prediction; CHAPTER 11: Nonlinear Regression; 11.1 Estimation for Nonlinear Mean Functions; 11.2 Inference Assuming Large Samples; 11.3 Starting Values; 11.4 Bootstrap Inference; 11.5 Further Reading
Includes bibliographical references and index.
Keywords: REGRESSION ANALYSIS