A Second Course in Statistics: Pearson New International Edition: Regression Analysis (7th edition)
By: Terry Sincich (author), William Mendenhall (author)Mixed Media
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The Second Course in Statistics is an increasingly important offering since more students are arriving at college having taken AP Statistics in high school. Mendenhall/Sincich's A Second Course in Statistics is the perfect book for courses that build on the knowledge students gain in AP Statistics, or the freshman Introductory Statistics course. 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 students to focus on specific problems, and are suitable for class discussion.
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 Variances 2. Introduction to Regression Analysis2.1 Modeling a Response2.2 Overview of Regression Analysis2.3 Regression Applications2.4 Collecting the Data for Regression 3. 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 s23.6 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) Case Study 1: Legal Advertising--Does It Pay? 4. 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 s2, the Variance of e4.6 Testing the Utility of a Model: The Analysis of Variance F-Test4.7 Inferences About the Individual ss Parameters4.8 Multiple Coefficients of Determination: R2 and R2adj4.9 Using the Model for Estimation and Prediction4.10 An Interaction Model with Quantitative Predictors4.11 A Quadratic (Second-Order) Model with a Quantitative Predictor4.12 More Complex Multiple Regression Models (Optional)4.13 A Test for Comparing Nested Models4.14 A Complete Example Case Study 2: Modeling the Sale Prices of Residential Properties in Four Neighborhoods 5. Principles of 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 6. Variable Screening Methods6.1 Introduction: Why Use a Variable-Screening Method?6.2 Stepwise Regression6.3 All-Possible-Regressions Selection Procedure6.4 Caveats Case Study 3: Deregulation of the Intrastate Trucking Industry 7. Some Regression Pitfalls7.1 Introduction7.2 Observational Data Versus Designed Experiments7.3 Parameter Estimability and Interpretation7.4 Multicollinearity7.5 Extrapolation: Predicting Outside the Experimental Region7.6 Variable Transformations 8. Residual Analysis8.1 Introduction8.2 Plotting Residuals8.3 Detecting Lack of Fit8.4 Detecting Unequal Variances8.5 Checking the Normality Assumption8.6 Detecting Outliers and Identifying Influential Observations8.7 Detection of Residual Correlation: The Durbin-Watson Test Case Study 4: An Analysis of Rain Levels in CaliforniaCase Study 5: An Investigation of Factors Affecting the Sale Price of Condominium Units Sold at Public Auction 9. Special Topics in Regression (Optional)9.1 Introduction9.2 Piecewise Linear Regression9.3 Inverse Prediction9.4 Weighted Least Squares9.5 Modeling Qualitative Dependent Variables9.6 Logistic Regression9.7 Ridge Regression9.8 Robust Regression9.9 Nonparametric Regression Models 10. 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) Case Study 6: Modeling Daily Peak Electricity Demands 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 Randomization 12. The Analysis of Variance for Designed Experiments12.1 Introduction12.2 The Logic Behind an 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 Assumptions Case Study 7: Reluctance to Transmit Bad News: The MUM Effect Appendix A: Derivation of the Least Squares Estimates of ss0 and ss1 in Simple Linear RegressionAppendix B: The Mechanics of a Multiple Regression AnalysisB.1 IntroductionB.2 Matrices and Matrix MultiplicationB.3 Identity Matrices and Matrix InversionB.4 Solving Systems of Simultaneous Linear EquationsB.5 The Least Squares Equations and Their SolutionB.6 Calculating SSE and s2B.7 Standard Errors of Estimators, Test Statistics, and Confidence Intervals for ss0, ss1, ... , sskB.8 A Confidence Interval for a Linear Function of the ss Parameters; A Confidence Interval for E(y)B.9 A Prediction Interval for Some Value of y to be Observed in the Future Appendix C: A Procedure for Inverting a Matrix Appendix D: Statistical TablesTable D.1: Normal Curve AreasTable D.2: Critical Values for Student's tTable D.3: Critical Values for the F Statistic: F.10Table D.4: Critical Values for the F Statistic: F.05Table D.5: Critical Values for the F Statistic: F.025Table D.6: Critical Values for the F Statistic: F.01Table D.7: Random NumbersTable D.8: Critical Values for the Durbin-Watson d Statistic (a =.05)Table D.9: Critical Values for the Durbin-Watson d Statistic (a =.01)Table D.10: Critical Values for the X2-StatisticTable D.11: Percentage Points of the Studentized Range, q(p,v), Upper 5%Table D.12: Percentage Points of the Studentized Range, q(p,v), Upper 1% Appendix E: File Layouts for Case Study Data Sets Answers to Selected Odd Numbered ExercisesIndexTechnology Tutorials: SAS, SPSS, MINITAB, and R (on CD)
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