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Applied Regression Analysis : Second Course in Business and Economic Statistics- With CD

Applied Regression Analysis : Second Course in Business and Economic Statistics- With CD - 4th edition

ISBN13: 978-0534465483

Cover of Applied Regression Analysis : Second Course in Business and Economic Statistics- With CD 4TH 05 (ISBN 978-0534465483)
ISBN13: 978-0534465483
ISBN10: 053446548X
Cover type: Hardback
Edition/Copyright: 4TH 05
Publisher: Brooks/Cole Publishing Co.
Published: 2005
International: No

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Applied Regression Analysis : Second Course in Business and Economic Statistics- With CD - 4TH 05 edition

ISBN13: 978-0534465483

Terry E. Dielman

ISBN13: 978-0534465483
ISBN10: 053446548X
Cover type: Hardback
Edition/Copyright: 4TH 05
Publisher: Brooks/Cole Publishing Co.

Published: 2005
International: No
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.

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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