Applied Regression Analysis A Second Course in Business and Economic Statistics (with CD-ROM and InfoTracï¿½)

Name: Applied Regression Analysis A Second Course in Business and Economic Statistics (with CD-ROM and InfoTracï¿½)
Price: 3.6 USD
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ISBN: 9780534465483

ISBN-10: 053446548X

ISBN-13: 9780534465483

Edition: 4th 2005 (Revised)

Authors: Terry E. Dielman

List price: $425.95

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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.

Book details

List price: $425.95
Edition: 4th
Copyright year: 2005
Publisher: Brooks/Cole
Publication date: 8/4/2004
Binding: Mixed Media
Pages: 496
Size: 7.72" wide x 9.53" long x 0.94" tall
Weight: 2.288
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 ï¿½Bootstrap versus Traditional Hypothesis Testing Procedures for Coefficients in Least Absolute Value Regressionï¿½ in the JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION. He participates in the Editorial Board of the…




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