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APPLIED REGRESSION ANALYSIS focuses on the application of regression to real data and examples while employing commercial statistical and spreadsheet software. Designed for both business/economics undergraduates and MBAs, this text provides all of the core regression topics as well as optional topics including ANOVA, Time Series Forecasting, and Discriminant Analysis. While only a prior introductory statistics course is required, a review of all necessary basic statistics is provided in chapter 2. The text emphasizes the importance of understanding the assumptions of the regression model, knowing how to validate a selected model for these assumptions, knowing when and how regression might be useful in a business setting, and understanding and interpreting output from statistical packages and spreadsheets.
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Terry Dielman is professor of Decision Sciences at Texas Christian University. Terry received his Ph.D. at the University of Michigan (Business Statistics), his M.S. at the University of Cincinnati (Mathematics) and his B.A. at Emporia State University (Mathematics). His recent research focuses on Regression Analysis, Time Series Forecasting, Robust Statistical Procedures and the Analysis of Pooled Cross-Sectional and Time Series Data. His recent publications include ┐Bootstrap versus Traditional Hypothesis Testing Procedures for Coefficients in Least Absolute Value Regression┐ in the JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION. He participates in the Editorial Board of the Journal of Business and Management, and consults for Forecasting Seminars and for various law firms.Review:
1. An Introduction to Regression Analysis. 2. Review of Basic Statistical Concepts. Introduction / Descriptive Statistics / Discrete Random Variables and Probability Distributions / The Normal Distribution / Populations, Samples, and Sampling Distributions / Estimating a Population Mean / Hypothesis Tests About a Population Mean / Estimating the Difference Between Two Population Means / Hypothesis Tests About the Difference Between Two Population Means. 3. Simple Regression Analysis. Using Simple Regression to Describe a Linear Relationship / Examples of Regression as a Descriptive Technique / Inferences from a Simple Regression Analysis / Assessing the Fit of the Regression Line / Prediction or Forecasting with a Simple Linear Regression Equation. Fitting a Linear Trend to Time-Series Data / Some Cautions in Interpreting Regression Results. 4. Multiple Regression Analysis. Using Multiple Regression to Describe a Linear Relationship / Inferences from a Multiple Regression Analysis / Assessing the Fit of the Regression Line / Comparing Two Regression Models / Prediction with a Multiple Regression Equation / Multicollinearity: A Potential Problem in Multiple Regression / Lagged Variables as Explanatory Variables in Time-Series Regression. 5. Fitting Curves to Data. Introduction / Fitting Curvilinear Relationships. 6. Assessing the Assumptions of the Regression Model. Introduction. Assumptions of the Multiple Linear Regression Model / The Regression Residuals / Assessing the Assumption That the Relationship is Linear / Assessing the Assumption That the Variance Around the Regression Line is Constant / Assessing the Assumption That the Disturbances are Normally Distributed / Influential observations / Assessing the Influence That the Disturbances are Independent. 7. Using Indicator and Interaction Variables. Using and Interpreting Indicator Variables / Interaction Variables / Seasonal Effects in Time-Series Regression. 8. Variable Selection. Introduction. All Possible Regressions. Other Variable Selection Techniques / Which Variable Selection Procedure is Best? 9. An Introduction to Analysis of Variance. One-Way Analysis of Variance. Analysis of Variance Using a Randomized Block Design / Two-Way Analysis of Variance / Analysis of Covariance. 10. Qualitative Dependent Variables: An Introduction to Discriminant Analysis and Logistic Regression. Introduction. Discriminant Analysis / Logistic Regression. 11. Forecasting Methods for Time-Series Data. Introduction / Naive Forecasts / Measuring Forecast Accuracy / Moving Averages / Exponential Smoothing / Decomposition. APPENDICES. A: Summation Notation. B: Statistical Tables. C: A Brief Introduction to MINITAB, Microsoft Excel, and SAS. D: Matrices and their Application to Regression Analysis. E: Solutions to Selected Odd-Numbered Exercises. References / Index.
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