A Second Course in Statistics: Regression Analysis: International Edition

For either the second term of a statistics course or for an advanced course in applied regression analysis for students in all disciplines, especially those in business. This very applied text focuses on building linear statistical models and developing skills for implementing regression analysis in real-life situations. It includes applications for engineering, sociology, psychology, etc., as well as traditional business applications. The authors use material from news articles, magazines, professional journals, and actual consulting problems to illustrate real-world problems and how to solve them using the tools of regression analysis. Datasets and other resources (where applicable) for this book are available here.

Table of Contents:
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) 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 s2 , the variance of e             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.