Chapter 12 Multiple Linear Regression and Certain Nonlinear Regression Models.

Chapter 12 Multiple Linear Regression and Certain Nonlinear Regression Models

Section 12.1 Introduction

Section 12.2 Estimating the Coefficients

Section 12.3 Linear Regression Model Using Matrices

Section 12.4 Properties of the Least Squares Estimators

Section 12.5 Inferences in Multiple Linear Regression

Figure 12.1 SAS printout for data in Example 12.4
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Section 12.6 Choice of a Fitted Model through Hypothesis Testing

Section 12.7 Special Cases of Orthogonality (Optional)

Table 12.4 Analysis of Variances for Orthogonal Variables

Table 12.6 Analysis of Variance for Grain Radius Data

Section 12.8 Categorical or Indicator Variables

Figure 12.2 Case of three categories

Figure 12.3 SAS printout for Example 12.9

Figure 12.4 Nonparallelism on categorical variables

Section 12.9 Sequential Methods for Model Selection

Table 12.8 Data Relating to Infant Length

Table 12.9 t-Values for the Regression Data of Table 12.8

Section 12.10 Study of Residuals and Violation of Assumptions (Model Checking)

Table 12.10 Data Set for Case Study 12.1

Table 12.11 Residual Information for the Data Set for Case Study 12.1

Section 12.11 Cross Validation, Cp, and Other Criteria for Model Selection

Table 12.12 Data Set Case Study 12.2

Table 12.13 Comparing Different Regression Models

Figure 12.6 SAS printout of all possible subsets on sales data for Example 12.12

Figure 12.7 Normal probability plot of residuals using the model x1x2x3 for Example 12.12

Section 12.12 Special Nonlinear Models for Nonideal Conditions

Figure 12.8 The logistic function

Table 12.16 Data Set for Example 12.13

Figure 12.9 SAS output for Review Exercise 12.72; part I

Figure 12.10 SAS output for Review Exercise 12.72; part II

Section 12.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

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