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Applied Regression Analysis A Second Course in Business and Economic Statistics

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ISBN-10: 053446548X

ISBN-13: 9780534465483

Edition: 4th 2005 (Revised)

Authors: Terry E. Dielman

List price: $366.95
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Description:

APPLIED REGRESSION ANALYSIS applies regression to real data and examples while employing commercial statistical and spreadsheet software. Covering the core regression topics as well as optional topics including ANOVA, Time Series Forecasting, and Discriminant Analysis, 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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Book details

List price: $366.95
Edition: 4th
Copyright year: 2005
Publisher: Brooks/Cole
Publication date: 8/4/2004
Binding: Hardcover
Pages: 496
Size: 7.50" wide x 9.20" long x 1.00" tall
Weight: 2.332
Language: English

Terry Dielman is professor of Decision Sciences at Texas Christian University. Terry received his Ph.D. at the University of Michigan (Business Statistics), his M.S. at the University of Cincinnati (Mathematics) and his B.A. at Emporia State University (Mathematics). His recent research focuses on Regression Analysis, Time Series Forecasting, Robust Statistical Procedures and the Analysis of Pooled Cross-Sectional and Time Series Data. His recent publications include �Bootstrap versus Traditional Hypothesis Testing Procedures for Coefficients in Least Absolute Value Regression� in the JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION. He participates in the Editorial Board of the Journal of Business and Management, and consults for Forecasting Seminars and for various law firms.

An Introduction to Regression Analysis
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
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
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
Fitting Curves to Data
Introduction
Fitting Curvilinear Relationships
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
Using Indicator and Interaction Variables
Using and Interpreting Indicator Variables
Interaction Variables
Seasonal Effects in Time-Series Regression
Variable Selection
Introduction
All Possible Regressions
Other Variable Selection Techniques
Which Variable Selection Procedure is Best?
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
Qualitative Dependent Variables: An Introduction to Discriminant Analysis and Logistic Regression
Introduction
Discriminant Analysis
Logistic Regression
Forecasting Methods for Time-Series Data
Introduction
Naive Forecasts
Measuring Forecast Accuracy
Moving Averages
Exponential Smoothing
Decomposition
Appendices
Summation Notation
Statistical Tables
A Brief Introduction to MINITAB, Microsoft Excel, and SAS
Matrices and their Application to Regression Analysis
Solutions to Selected Odd-Numbered Exercises
References
Index