xii CONTENTS10-6 Inference on Two Population
Proportions 361
10-6.1 Large-Sample Test for
H 0 : p 1 p 2 361
10-6.2 Small Sample Test for
H 0 : p 1 p 2 (CD Only) 364
10-6.3-Error and Choice of
Sample Size 364
10-6.4 Confidence Interval for
P 1 P 2 365
10-7 Summary Table for Inference
Procedures for Two Samples 367CHAPTER 11 Simple Linear
Regression and Correlation 372
11-1 Empirical Models 373
11-2 Simple Linear Regression 375
11-3 Properties of the Least Squares
Estimators 383
11-4 Some Comments on Uses of
Regression (CD Only) 384
11-5 Hypothesis Tests in Simple Linear
Regression 384
11-5.1 Use of t-Tests 384
11-5.2 Analysis of Variance Approach
to Test Significance of
Regression 387
11-6 Confidence Intervals 389
11-6.1 Confidence Intervals on the
Slope and Intercept 389
11-6.2 Confidence Interval on the
Mean Response 390
11-7 Prediction of New Observations 392
11-8 Adequacy of the Regression
Model 395
11-8.1 Residual Analysis 395
11-8.2 Coefficient of Determination
(R^2 ) 397
11-8.3 Lack-of-Fit Test
(CD Only) 398
11-9 Transformations to a Straight
Line 400
11-10 More About Transformations
(CD Only) 400
11-11 Correlation 400CHAPTER 12 Multiple Linear
Regression 410
12-1 Multiple Linear Regression
Model 41112-1.1 Introduction 411
12-1.2 Least Squares Estimation of the
Parameters 414
12-1.3 Matrix Approach to Multiple
Linear Regression 417
12-1.4 Properties of the Least Squares
Estimators 421
12-2 Hypothesis Tests in Multiple Linear
Regression 428
12-2.1 Test for Significance of
Regression 428
12-2.2 Tests on Individual Regression
Coefficients and Subsets of
Coefficients 432
12-2.3 More About the Extra Sum of
Squares Method (CD Only) 435
12-3 Confidence Intervals in Multiple
Linear Regression 437
12-3.1 Confidence Intervals on Individual
Regression Coefficients 437
12-3.2 Confidence Interval on the Mean
Response 438
12-4 Prediction of New Observations 439
12-5 Model Adequacy Checking 441
12-5.1 Residual Analysis 441
12-5.2 Influential Observations 444
12-6 Aspects of Multiple Regression
Modeling 447
12-6.1 Polynomial Regression
Models 447
12-6.2 Categorical Regressors and
Indicator Variables 450
12-6.3 Selection of Variables and Model
Building 452
12-6.4 Multicollinearity 460
12-6.5 Ridge Regression
(CD Only) 461
12-6.6 Nonlinear Regression Models
(CD Only) 461CHAPTER 13 Design and
Analysis of Single-Factor
Experiments: The Analysis
of Variance 468
13-1 Designing Engineering
Experiments 469
13-2 The Completely Randomized
Single-Factor Experiment 470
13-2.1 An Example 470
13-2.2 The Analysis of Variance 472
13-2.3 Multiple Comparisons Following
the ANOVA 479PQ220 6234F.FM 5/30/02 1:02 PM Page xii RK UL 6 RK UL 6:Desktop Folder:untitled folder: