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# Applied Regression Analysis

### A Second Course in Business and Economic Statistics (with CD-ROM and InfoTracï½)

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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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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.

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 / Naï½ve 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.

ISBN: 9780534465483
ISBN-10: 053446548X
Series: Applied Regression Analysis: A Second Course in Business & Economic
Audience: Tertiary; University or College
Format: Hardcover
Language: English
Number Of Pages: 496
Published: 3rd September 2004
Country of Publication: US
Dimensions (cm): 24.2 x 19.6  x 2.6
Weight (kg): 0.91
Edition Number: 4
Edition Type: Revised

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