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.
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.