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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.

**New to the Edition**

- A new chapter 11, "Forecasting Methods for Time Series Data," is included in addition to the time series presentation provided in context in selected chapters throughout the book.
- Chapters now feature generic high-resolution graphics and output within the chapters. All software specific output, graphics and instructions now appear at the conclusion of each chapter. This new organization is designed to accommodate a broad range of computing preferences.
- SAS output and instruction are now included (in addition to MINITAB and Excel).
- Many new exercises and updated data are included in the 4th edition.

**Features**

- Most data comes from actual business problems culled from journals and popular business publications.
- Time Series models are introduced after their respective cross-sectional models throughout the text.
- SAS, MINITAB, and Excel procedures used to perform analyses are presented in a "Using a Computer" section at the end of each chapter. In addition, there is a brief introduction to each of these programs in Appendix C.

**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.

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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.

**New to the Edition**

- A new chapter 11, "Forecasting Methods for Time Series Data," is included in addition to the time series presentation provided in context in selected chapters throughout the book.
- Chapters now feature generic high-resolution graphics and output within the chapters. All software specific output, graphics and instructions now appear at the conclusion of each chapter. This new organization is designed to accommodate a broad range of computing preferences.
- SAS output and instruction are now included (in addition to MINITAB and Excel).
- Many new exercises and updated data are included in the 4th edition.

**Features**

- Most data comes from actual business problems culled from journals and popular business publications.
- Time Series models are introduced after their respective cross-sectional models throughout the text.
- SAS, MINITAB, and Excel procedures used to perform analyses are presented in a "Using a Computer" section at the end of each chapter. In addition, there is a brief introduction to each of these programs in Appendix C.

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.

Publisher Info

Publisher: Brooks/Cole Publishing Co.

Published: 2005

International: No

Published: 2005

International: No

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.

**New to the Edition**

- A new chapter 11, "Forecasting Methods for Time Series Data," is included in addition to the time series presentation provided in context in selected chapters throughout the book.
- Chapters now feature generic high-resolution graphics and output within the chapters. All software specific output, graphics and instructions now appear at the conclusion of each chapter. This new organization is designed to accommodate a broad range of computing preferences.
- SAS output and instruction are now included (in addition to MINITAB and Excel).
- Many new exercises and updated data are included in the 4th edition.

**Features**

- Most data comes from actual business problems culled from journals and popular business publications.
- Time Series models are introduced after their respective cross-sectional models throughout the text.
- SAS, MINITAB, and Excel procedures used to perform analyses are presented in a "Using a Computer" section at the end of each chapter. In addition, there is a brief introduction to each of these programs in Appendix C.

**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.