This reader-friendly book focuses on building linear statistical models and developing skills for implementing regression analysis in real-life situations. It includes applications for a range of fields including engineering, sociology, and psychology, as well as traditional business applications. The authors use the latest material available from news articles, magazines, professional journals, the Internet, and actual consulting problems to illustrate real business situations and how to solve them using the tools of regression analysis. In addition, this book emphasizes model building and multiple regression models and pays special attention to model validation and spline regression. For professionals in any number of fields, including engineering, sociology, and psychology, who would benefit from learning how to use regression analysis to solve 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.