Design and Analysis of Experiments, 9th Edition

CHAPTER 10Fitting Regression Models

CHAPTER OUTLINE

1. 10.1 INTRODUCTION
2. 10.2 LINEAR REGRESSION MODELS
3. 10.3 ESTIMATION OF THE PARAMETERS IN LINEAR REGRESSION MODELS
4. 10.4 HYPOTHESIS TESTING IN MULTIPLE REGRESSION
   1. 10.4.1 Test for Significance of Regression
   2. 10.4.2 Tests on Individual Regression Coefficients and Groups of Coefficients
5. 10.5 CONFIDENCE INTERVALS IN MULTIPLE REGRESSION
   1. 10.5.1 Confidence Intervals on the Individual Regression Coefficients
   2. 10.5.2 Confidence Interval on the Mean Response
6. 10.6 PREDICTION OF NEW RESPONSE OBSERVATIONS
7. 10.7 REGRESSION MODEL DIAGNOSTICS
   1. 10.7.1 Scaled Residuals and PRESS
   2. 10.7.2 Influence Diagnostics
8. 10.8 TESTING FOR LACK OF FIT

SUPPLEMENTAL MATERIAL FOR CHAPTER 10

1. S10.1 The Covariance Matrix of the Regression Coefficients
2. S10.2 Regression Models and Designed Experiments
3. S10.3 Adjusted R²
4. S10.4 Stepwise and Other Variable Selection Methods in Regression
5. S10.5 The Variance of the Predicted Response
6. S10.6 The Variance of Prediction Error
7. S10.7 Leverage in a Regression Model

The supplemental material is on the textbook website www.wiley.com/college/montgomery.

CHAPTER LEARNING OBJECTIVES

1. Understand fitting linear regression models using the method of least squares.
2. Understand basic regression model inference, including tests for significance of regression, tests on individual model parameters and confidence intervals on the parameters.
3. Know how to use the model to estimate the mean response at a specific point and construct the associated ...