1. A Review of Basic Concepts (Optional)

1.1 Statistics and Data

1.2 Populations, Samples, and Random Sampling

1.3 Describing Qualitative Data

1.4 Describing Quantitative Data Graphically

1.5 Describing Quantitative Data Numerically

1.6 The Normal Probability Distribution

1.7 Sampling Distributions and the Central Limit Theorem

1.8 Estimating a Population Mean

1.9 Testing a Hypothesis About a Population Mean

1.10 Inferences About the Difference Between Two Population Means

1.11 Comparing Two Population Variances

2. Introduction to Regression Analysis

2.1 Modeling a Response

2.2 Overview of Regression Analysis

2.3 Regression Applications

2.4 Collecting the Data for Regression

3. Simple Linear Regression

3.1 Introduction

3.2 The Straight-Line Probabilistic Model

3.3 Fitting the Model: The Method of Least Squares

3.4 Model Assumptions

3.5 An Estimator of s2

3.6 Assessing the Utility of the Model: Making Inferences About the Slope ß1

3.7 The Coefficient of Correlation

3.8 The Coefficient of Determination

3.9 Using the Model for Estimation and Prediction

3.10 A Complete Example

3.11 Regression Through the Origin (Optional)

Case Study 1: Legal Advertising--Does It Pay?

4. Multiple Regression Models

4.1 General Form of a Multiple Regression Model

4.2 Model Assumptions

4.3 A First-Order Model with Quantitative Predictors

4.4 Fitting the Model: The Method of Least Squares

4.5 Estimation of s2, the Variance of e

4.6 Testing the Utility of a Model: The Analysis of Variance F-Test

4.7 Inferences About the Individual ß Parameters

4.8 Multiple Coefficients of Determination: R2 and R2adj

4.9 Using the Model for Estimation and Prediction

4.10 An Interaction Model with Quantitative Predictors

4.11 A Quadratic (Second-Order) Model with a Quantitative Predictor

4.12 More Complex Multiple Regression Models (Optional)

4.13 A Test for Comparing Nested Models

4.14 A Complete Example

Case Study 2: Modeling the Sale Prices of Residential Properties in Four Neighborhoods

5. Principles of Model Building

5.1 Introduction: Why Model Building is Important

5.2 The Two Types of Independent Variables: Quantitative and Qualitative

5.3 Models with a Single Quantitative Independent Variable

5.4 First-Order Models with Two or More Quantitative Independent Variables

5.5 Second-Order Models with Two or More Quantitative Independent Variables

5.6 Coding Quantitative Independent Variables (Optional)

5.7 Models with One Qualitative Independent Variable

5.8 Models with Two Qualitative Independent Variables

5.9 Models with Three or More Qualitative Independent Variables

5.10 Models with Both Quantitative and Qualitative Independent Variables

5.11 External Model Validation

6. Variable Screening Methods

6.1 Introduction: Why Use a Variable-Screening Method?

6.2 Stepwise Regression

6.3 All-Possible-Regressions Selection Procedure

6.4 Caveats

Case Study 3: Deregulation of the Intrastate Trucking Industry

7. Some Regression Pitfalls

7.1 Introduction

7.2 Observational Data Versus Designed Experiments

7.3 Parameter Estimability and Interpretation

7.4 Multicollinearity

7.5 Extrapolation: Predicting Outside the Experimental Region

7.6 Variable Transformations

8. Residual Analysis

8.1 Introduction

8.2 Plotting Residuals

8.3 Detecting Lack of Fit

8.4 Detecting Unequal Variances

8.5 Checking the Normality Assumption

8.6 Detecting Out

• Readability was a main goal of the authors, whose intent was to create a teaching text rather than a reference text. Concepts are explained in a logical, intuitive manner with worked-out examples.
• Model building is fundamental to any regression analysis and is introduced in Chapters 4—8, then emphasised throughout the text.
• Development of regression skills: in addition to teaching basic concepts and methodology, this text stresses its usage in solving applied problems.
• Real data is used in examples and exercises to maintain the applied nature of this text.
• Examples illustrate the important aspects of model construction, data analysis, and the interpretation of results.
• Exercises are located at the end of every section and chapter. Nearly every exercise is based on data and research extracted from news or journal articles.
• Seven case studies address real-life research problems and are suitable for class discussion. While working through these, students can see how regression analysis is used to answer practical questions, and can then formulate appropriate statistical models for the analysis and interpretation of sample data.
• Statistical software instruction includes the latest software packages: SAS®, SPSS®, MINITAB®

• New and updated case studies: two new case studies (Case Study 1: Legal Advertising–Does it Pay? and Case Study 3: Deregulation of the Trucking Industry) have been added, and another (Case Study 2: Modeling Sale Prices of Residential Properties) has been updated with current data. Also, all seven of the case studies now follow the relevant chapter material for improved logical flow.
• Real data exercises have been increased and updated, based on contemporary studies and developments in a variety of fields.
• Statistical software instruction includes the latest software packages: SAS®, SPSS®, MINITAB®, and, new to the text, R. Tutorials are now provided on the CD-ROM, and printouts associated with the software are presented and discussed throughout the text.
• More emphasis onp-values reflects the tendency of regression analysts to rely on statistical software to fit and assess models in practice. This prepares students for testing statistical hypothesis theories with technology used by professional statisticians.
• Updated examples in Chapter 9, Special Topics in Regression, include new examples on piecewise regression, weighted least squares, logistic regression, and ridge regression.
• Redesigned end-of-chapter summaries are easier to use when reviewing and studying.
  • Flow charts aid in the selection of the appropriate statistical method.
  • Important points are reinforced through boxed notes with key words, formulas, definitions, lists, and key concepts.