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Cover type: Hardback

Edition: 4TH 05

Copyright: 2005

Publisher: Brooks/Cole Publishing Co.

Published: 2005

International: No

Edition: 4TH 05

Copyright: 2005

Publisher: Brooks/Cole Publishing Co.

Published: 2005

International: No

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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. **

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.

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Summary

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. **

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.

Publisher Info

Publisher: Brooks/Cole Publishing Co.

Published: 2005

International: No

Published: 2005

International: No

**1. An Introduction to Regression Analysis. **

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.