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Section 12.1
Introduction
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Section 12.2
Estimating the
Coefficients
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Table 12.1 Data for Example 12.1
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Table 12.2 Data for Example 12.3
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Table 12.3 Data for Example 12.4
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Section 12.4
Properties of the Least Squares
Estimators
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Theorem 12.1
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Section 12.5
Inferences in
Multiple Linear
Regression
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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
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Section 12.7
Special Cases of Orthogonality
(Optional)
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Table 12.4 Analysis of Variances
for Orthogonal Variables
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Table 12.5 Data for Example 12.8
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Table 12.6 Analysis of Variance
for Grain Radius Data
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Section 12.8
Categorical or
Indicator
Variables
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Figure 12.2 Case of three
categories
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Table 12.7 Data for Example 12.9
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Figure 12.3 SAS printout for
Example 12.9
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Figure 12.4 Nonparallelism on
categorical variables
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Section 12.9
Sequential
Methods for
Model Selection
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Table 12.8 Data Relating to Infant
Length
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Table 12.9 t-Values for the
Regression Data of Table 12.8
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Table 12.10 Data Set for Case
Study 12.1
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the Data Set for Case Study 12.1
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Figure 12.5 R-Student values plotted
against predicted values for
grasshopper data of Case Study 12.1
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Table 12.12 Data Set Case Study
12.2
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Table 12.13 Comparing Different
Regression Models
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Table 12.14 PRESS Residuals
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Table 12.15 Data for Example
12.12
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Figure 12.6 SAS printout of all
possible subsets on sales data for
Example 12.12
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Figure 12.7 Normal probability plot of
residuals using the model x 1 x 2 x 3 for
Example 12.12
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Section 12.12
Special Nonlinear Models for
Nonideal
Conditions
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Figure 12.8 The logistic function
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Table 12.16 Data Set for Example
12.13
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Definition 12.1
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Figure 12.9 SAS output for
Review Exercise 12.72; part I
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Exercise 12.72; part II
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Potential
Misconceptions and Hazards;
Relationship to
Material in Other Chapters
Section 12.13