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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.
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 / Naïve 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.
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.
Table of Contents
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 / Naïve 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.