A Second Course in Statistics: Regression Analysis
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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 Data1.2 Populations, Samples and Random Sampling1.3 Describing Qualitative Data1.4 Describing Quantitative Data Graphically1.5 Describing Quantitative Data Numerically1.6 The Normal Probability Distribution1.7 Sampling Distributions and the Central Limit Theorem1.8 Estimating a Population Mean1.9 Testing a Hypothesis about a Population mean1.10 Inferences about the Difference Between Two Population Means1.11 Comparing Two Population Variances2. Introduction to Regression Analysis2.1 Modeling a Response2.2 overview of Regression Analysis2.3 Regression Applications2.4 Collecting the Data for Regression3. Simple Linear Regression3.1 Introduction3.2 The Straight-Line Probabilistic Model3.3 Fitting the Model: The Method of Least-Squares3.4 Model Assumptions3.5 An Estimator of σ23.6 Assessing the Utility of the Model: Making Inferences About the Slope ß13.7 The Coefficient of Correlation3.8 The Coefficient of Determination3.9 Using the Model for Estimation and Prediction3.10 A Complete Example3.11 Regression Through the Origin (Optional)3.12 A Summary of the Steps to Follow in a Simple Linear Regression Analysis4. Multiple Regression Models4.1 General Form of a Multiple Regression Model4.2 Model Assumptions4.3 A First-Order Model with Quantitative Predictors4.4 Fitting the Model: The Method of Least Squares4.5 Estimation of σ2 , the variance of ε4.6 Inferences about the ß parameters4.7 The Multiple Coefficient of Determination, R2 4.8 Testing the Utility of a Model: The Analysis of Variance F test4.9 An Interaction Model with Quantitative Predictors4.10 A Quadratic (Second-Order) Model with a Quantitative Predictor4.11 Using the model for Estimation and Prediction4.12 More Complex Multiple Regression Models (Optional)4.13 A Test for Comparing Nested Models4.14 A Complete Example4.15 A Summary of the Steps to Follow in a Multiple Regression Analysis5. Model Building5.1 Introduction: Why Model Building is Important5.2 The Two Types of independent Variables: Quantitative and Qualitative5.3 Models with a Single Quantitative Independent Variable5.4 First-Order Models with Two or More Quantitative Independent Variables5.5. Second-Order Models with Two or More Quantitative Independent Variables5.6 Coding Quantitative Independent Variables (Optional)5.7 Models with One Qualitative Independent Variable5.8 Models with Two Qualitative Independent Variables5.9 Models with Three or more Qualitative Independent Variables5.10 Models with Both Quantitative and Qualitative Independent Variables5.11 External Model Validation (Optional)5.12 Model Building: An Example6. Variable Screening Methods6.1 Introduction: Why Use a Variable Screening Method?6.2 Stepwise Regression6.3 All-Posssible-Regressions Selection Procedure6.4 Caveats7. Some Regression Pitfalls7.1 Introduction7.2 Observational DataVersus Designed Experiments7.3 Deviating from the Assumptions7.4 Parameter Estimability and Interpretation7.5 Multicollinearity7.6 Extrapolation: Predicting Outside the Experimental Region7.7 Data Transformations8. Residual Analysis8.1 Introduction8.2 Plotting Residuals and Detecting Lack of Fit8.3 Detecting Unequal Variances8.4 Checking the Normality Assumption8.5 Detecting Outliers and Identifying Influential Observations8.6 Detecting Residual Correlation: The Durbin-Watson Test9. Special Topics in Regression (Optional)9.1 Introduction9.2 Piecewise Linear Regression9.3 Inverse Prediction9.4 Weighted Least Squares9.5 Modeling Qualitative Dependent Variable9.6 Logistic Regression9.7 Ridge Regression9.8 Robust Regression9.9 Nonparametric Regression Models10. Introduction to Time Series Modeling and Forecasting10.1 What is a Time Series?10.2 Time Series Components10.3 Forecasting using Smoothing Techniques (Optional)10.4 Forecasting: The Regression Approach10.5 Autocorrelation and Autoregressive Error Models10.6 Other Models for Autocorrelated Errors (Optional)10.7 Constructing Time Series Models10.8 Fitting Time Series Models With Autoregressive Errors10.9 Forecasting with Time Series Autoregressive Models10.10 Seasonal Time Series Models: An Example10.11 Forecasting Using Lagged Values of the Dependent Variable (Optional)11. Principles of Experimental Design11.1 Introduction11.2 Experimental Design Terminology11.3 Controlling the Information in an Experiment11.4 Noise-Reducing Designs11.5 Volume-Increasing Designs11.6 Selecting the Sample Size11.7 The Importance of Randomization12. The Analysis of Variance for Designed Experiments12.1 Introduction12.2 The Logic Behind Analysis of Variance12.3. One-Factor Completely Randomized Designs12.4 Randomized Block Designs12.5 Two-Factor Factorial Experiments12.6 More Complex Factorial Designs (Optional)12.7 Follow up Analysis: Tukey’s Multiple Comparisons of Means12.8 Other Multiple Comparisons Methods (Optional)12.9 Checking ANOVA Assumptions13. CASE STUDY: Modeling the Sale Prices of Residential Properties in Four Neighborhoods13.1 The Problem13.2 The Data13.3 The Theoretical Model13.4 The Hypothesized Regression Models13.5 Model Comparisons13.6 Interpreting the Prediction Equation13.7 Predicting the Sale Price of a Property13.8 Conclusions14. CASE STUDY: An Analysis of Rain Levels in California14.1 The Problem14.2 The Data14.3 A Model for Average Annual Precipitation14.4 A Residual Analysis of the Model14.5 Adjustments to the Model14.6 Conclusions15. CASE STUDY: Reluctance to Transmit Bad News: the MUM Effect15.1 The Problem15.2 The Design15.3 Analysis of Variance Models and Results15.4 Follow up Analysis15.5 Conclusions16. CASE STUDY: An Investigation of Factors Affecting the Sale Price of Condominium Units Sold at Public Auction16.1 The Problem16.2 The Data16.3 The Models16.4 The Regression Analyses16.5 An Analysis of the Residuals form Model 316.6 What the Model 3 Regression Analysis Tells Us16.7 Comparing the Mean Sale Price for Two Types of Units (Optional)16.8 Conclusions17. CASE STUDY: Modeling Daily Peak Electricity Demands17.1 The Problem17.2 The Data17.3 The Models17.4 The Regression and Autoregression Analyses17.5 Forecasting Daily Peak Electricity Demand17.6 ConclusionsAppendix 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.
