Applied Regression Analysis for Business and Economics

ISBN-10: 0534379559

ISBN-13: 9780534379551

Edition: 3rd 2001

Authors: Terry E. Dielman

List price: $142.95
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Book details

List price: $142.95
Edition: 3rd
Copyright year: 2001
Publisher: Brooks/Cole
Publication date: 7/31/2000
Binding: Hardcover
Pages: 656
Size: 7.25" wide x 9.25" long x 1.00" tall
Weight: 2.530
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 Analysisp. 1
Review of Basic Statistical Conceptsp. 5
Introductionp. 5
Descriptive Statisticsp. 7
Discrete Random Variables and Probability Distributionsp. 21
The Normal Distributionp. 27
Populations, Samples, and Sampling Distributionsp. 35
Estimating a Population Meanp. 39
Hypothesis Tests About a Population Meanp. 45
Estimating the Difference Between Two Population Meansp. 53
Hypothesis Tests About the Difference Between Two Population Meansp. 62
Using the Computerp. 67
Simple Regression Analysisp. 87
Using Simple Regression to Describe a Linear Relationshipp. 87
Examples of Regression as a Descriptive Techniquep. 95
Inferences from a Simple Regression Analysisp. 100
Assessing the Fit of the Regression Linep. 123
Prediction or Forecasting with a Simple Linear Regression Equationp. 132
Fitting a Linear Trend to Time-Series Datap. 144
Some Cautions in Interpreting Regression Resultsp. 152
Using the Computerp. 153
Multiple Regression Analysisp. 171
Using Multiple Regression to Describe a Linear Relationshipp. 171
Inferences from a Multiple Regression Analysisp. 175
Assessing the Fit of the Regression Linep. 181
Comparing Two Regression Modelsp. 191
Prediction with a Multiple Regression Equationp. 204
Lagged Variables as Explanatory Variables in Time-Series Regressionp. 206
Using the Computerp. 220
Fitting Curves to Datap. 247
Introductionp. 247
Fitting a Curvilinear Relationshipp. 248
Using the Computerp. 281
Assessing the Assumptions of the Regression Modelp. 291
Introductionp. 291
Assumptions of the Multiple Linear Regression Modelp. 292
The Regression Residualsp. 292
Assessing the Assumption That the Relationship Is Linearp. 296
Assessing the Assumption That the Variance Around the Regression Line Is Constantp. 312
Assessing the Assumption That the Disturbances Are Normally Distributedp. 328
Influential Observationsp. 340
Assessing the Assumption That the Disturbances Are Independentp. 357
Multicollinearityp. 366
Using the Computerp. 369
Using Indicator and Interaction Variablesp. 391
Using and Interpreting Indicator Variablesp. 391
Interaction Variablesp. 415
Seasonal Effects in Time-Series Regressionp. 427
Using the Computerp. 443
Variable Selectionp. 473
Introductionp. 473
All Possible Regressionsp. 474
Other Variable Selection Techniquesp. 480
Which Variable Selection Procedure Is Best?p. 482
Using the Computerp. 487
An Introduction to Analysis of Variancep. 511
One-Way Analysis of Variancep. 511
Analysis of Variance Using a Randomized Block Designp. 525
Two-Way Analysis of Variancep. 535
Analysis of Covariancep. 546
Using the Computerp. 546
Qualitative Dependent Variables: An Introduction to Discriminant Analysis and Logistic Regressionp. 561
Introductionp. 561
Discriminant Analysisp. 563
Logistic Regressionp. 570
Using the Computerp. 574
Summation Notationp. 579
Statistical Tablesp. 581
A Brief Introduction to MINITAB, Microsoft Excel, and SASp. 593
Matrices and Their Application to Regression Analysisp. 615
Solutions to Selected Odd-Numbered Exercisesp. 625
Referencesp. 639
Indexp. 641
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