A Second Course in Statistics: Regression Analysis » (6th Edition)

A Second Course in Statistics: Regression Analysis, Seventh Edition, focuses on building linear statistical models and developing skills for implementing regression analysis in real situations. This text offers applications for engineering, sociology, psychology, science, and business. The authors use real data and scenarios extracted from news articles, journals, and actual consulting problems to show how to apply the concepts. In addition, seven case studies, now located throughout the text after applicable chapters, invite readers to focus on specific problems.

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 σ²

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)

3.12 A Summary of the Steps to Follow in a Simple Linear Regression Analysis

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 σ² , the variance of ε

4.6 Inferences about the ß parameters

4.7 The Multiple Coefficient of Determination, R²

4.8 Testing the Utility of a Model: The Analysis of Variance F test

4.9 An Interaction Model with Quantitative Predictors

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

4.11 Using the model for Estimation and Prediction

4.12 More Complex Multiple Regression Models (Optional)

4.13 A Test for Comparing Nested Models

4.14 A Complete Example

4.15 A Summary of the Steps to Follow in a Multiple Regression Analysis

5. 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 (Optional)

5.12 Model Building: An Example

6. Variable Screening Methods

6.1 Introduction: Why Use a Variable Screening Method?

6.2 Stepwise Regression

6.3 All-Posssible-Regressions Selection Procedure

6.4 Caveats

7. Some Regression Pitfalls

7.1 Introduction

7.2 Observational DataVersus Designed Experiments

7.3 Deviating from the Assumptions

7.4 Parameter Estimability and Interpretation

7.5 Multicollinearity

7.6 Extrapolation: Predicting Outside the Experimental Region

7.7 Data Transformations

8. Residual Analysis

8.1 Introduction

8.2 Plotting Residuals and Detecting Lack of Fit

8.3 Detecting Unequal Variances

8.4 Checking the Normality Assumption

8.5 Detecting Outliers and Identifying Influential Observations

8.6 Detecting Residual Correlation: The Durbin-Watson Test

9. Special Topics in Regression (Optional)

9.1 Introduction

9.2 Piecewise Linear Regression

9.3 Inverse Prediction

9.4 Weighted Least Squares

9.5 Modeling Qualitative Dependent Variable

9.6 Logistic Regression

9.7 Ridge Regression

9.8 Robust Regression

9.9 Nonparametric Regression Models

10. Introduction to Time Series Modeling and Forecasting

10.1 What is a Time Series?

10.2 Time Series Components

10.3 Forecasting using Smoothing Techniques (Optional)

10.4 Forecasting: The Regression Approach

10.5 Autocorrelation and Autoregressive Error Models

10.6 Other Models for Autocorrelated Errors (Optional)

10.7 Constructing Time Series Models

10.8 Fitting Time Series Models With Autoregressive Errors

10.9 Forecasting with Time Series Autoregressive Models

10.10 Seasonal Time Series Models: An Example

10.11 Forecasting Using Lagged Values of the Dependent Variable (Optional)

11. Principles of Experimental Design

11.1 Introduction

11.2 Experimental Design Terminology

11.3 Controlling the Information in an Experiment

11.4 Noise-Reducing Designs

11.5 Volume-Increasing Designs

11.6 Selecting the Sample Size

11.7 The Importance of Randomization

12. The Analysis of Variance for Designed Experiments

12.1 Introduction

12.2 The Logic Behind Analysis of Variance

12.3. One-Factor Completely Randomized Designs

12.4 Randomized Block Designs

12.5 Two-Factor Factorial Experiments

12.6 More Complex Factorial Designs (Optional)

12.7 Follow up Analysis: Tukey’s Multiple Comparisons of Means

12.8 Other Multiple Comparisons Methods (Optional)

12.9 Checking ANOVA Assumptions

13. CASE STUDY: Modeling the Sale Prices of Residential Properties in Four Neighborhoods

13.1 The Problem

13.2 The Data

13.3 The Theoretical Model

13.4 The Hypothesized Regression Models

13.5 Model Comparisons

13.6 Interpreting the Prediction Equation

13.7 Predicting the Sale Price of a Property

13.8 Conclusions

14. CASE STUDY: An Analysis of Rain Levels in California

14.1 The Problem

14.2 The Data

14.3 A Model for Average Annual Precipitation

14.4 A Residual Analysis of the Model

14.5 Adjustments to the Model

14.6 Conclusions

15. CASE STUDY: Reluctance to Transmit Bad News: the MUM Effect

15.1 The Problem

15.2 The Design

15.3 Analysis of Variance Models and Results

15.4 Follow up Analysis

15.5 Conclusions

16. CASE STUDY: An Investigation of Factors Affecting the Sale Price of Condominium Units Sold at Public Auction

16.1 The Problem

16.2 The Data

16.3 The Models

16.4 The Regression Analyses

16.5 An Analysis of the Residuals form Model 3

16.6 What the Model 3 Regression Analysis Tells Us

16.7 Comparing the Mean Sale Price for Two Types of Units (Optional)

16.8 Conclusions

17. CASE STUDY: Modeling Daily Peak Electricity Demands

17.1 The Problem

17.2 The Data

17.3 The Models

17.4 The Regression and Autoregression Analyses

17.5 Forecasting Daily Peak Electricity Demand

17.6 Conclusions

Appendix A: The Mechanics of a Multiple Regression Analysis.

Appendix B: A Procedure for Inverting a Matrix.

Appendix C: Statistical Tables.

Appendix D: SAS for Windows Tutorial.

Appendix E: SPSS for Windows Tutorial.

Appendix F: MINITAB for Windows Tutorial.

Appendix G: Sealed Bid Data for Fixed and Competitive Highway Construction Contracts.

Appendix H: Real Estate Appraisals and Sales Data for Six Neighborhoods in Tampa, Florida.

Appendix I: Condominium Sales Data.

Answers to Odd-Numbered Exercises.

Index.