Applied Regression Analysis for Business and Economics

Name: Applied Regression Analysis for Business and Economics
Price: 5.58 USD
Availability: InStock
ISBN: 9780534379551

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 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	p. 1
Review of Basic Statistical Concepts	p. 5
Introduction	p. 5
Descriptive Statistics	p. 7
Discrete Random Variables and Probability Distributions	p. 21
The Normal Distribution	p. 27
Populations, Samples, and Sampling Distributions	p. 35
Estimating a Population Mean	p. 39
Hypothesis Tests About a Population Mean	p. 45
Estimating the Difference Between Two Population Means	p. 53
Hypothesis Tests About the Difference Between Two Population Means	p. 62
Using the Computer	p. 67
Simple Regression Analysis	p. 87
Using Simple Regression to Describe a Linear Relationship	p. 87
Examples of Regression as a Descriptive Technique	p. 95
Inferences from a Simple Regression Analysis	p. 100
Assessing the Fit of the Regression Line	p. 123
Prediction or Forecasting with a Simple Linear Regression Equation	p. 132
Fitting a Linear Trend to Time-Series Data	p. 144
Some Cautions in Interpreting Regression Results	p. 152
Using the Computer	p. 153
Multiple Regression Analysis	p. 171
Using Multiple Regression to Describe a Linear Relationship	p. 171
Inferences from a Multiple Regression Analysis	p. 175
Assessing the Fit of the Regression Line	p. 181
Comparing Two Regression Models	p. 191
Prediction with a Multiple Regression Equation	p. 204
Lagged Variables as Explanatory Variables in Time-Series Regression	p. 206
Using the Computer	p. 220
Fitting Curves to Data	p. 247
Introduction	p. 247
Fitting a Curvilinear Relationship	p. 248
Using the Computer	p. 281
Assessing the Assumptions of the Regression Model	p. 291
Introduction	p. 291
Assumptions of the Multiple Linear Regression Model	p. 292
The Regression Residuals	p. 292
Assessing the Assumption That the Relationship Is Linear	p. 296
Assessing the Assumption That the Variance Around the Regression Line Is Constant	p. 312
Assessing the Assumption That the Disturbances Are Normally Distributed	p. 328
Influential Observations	p. 340
Assessing the Assumption That the Disturbances Are Independent	p. 357
Multicollinearity	p. 366
Using the Computer	p. 369
Using Indicator and Interaction Variables	p. 391
Using and Interpreting Indicator Variables	p. 391
Interaction Variables	p. 415
Seasonal Effects in Time-Series Regression	p. 427
Using the Computer	p. 443
Variable Selection	p. 473
Introduction	p. 473
All Possible Regressions	p. 474
Other Variable Selection Techniques	p. 480
Which Variable Selection Procedure Is Best?	p. 482
Using the Computer	p. 487
An Introduction to Analysis of Variance	p. 511
One-Way Analysis of Variance	p. 511
Analysis of Variance Using a Randomized Block Design	p. 525
Two-Way Analysis of Variance	p. 535
Analysis of Covariance	p. 546
Using the Computer	p. 546
Qualitative Dependent Variables: An Introduction to Discriminant Analysis and Logistic Regression	p. 561
Introduction	p. 561
Discriminant Analysis	p. 563
Logistic Regression	p. 570
Using the Computer	p. 574
Summation Notation	p. 579
Statistical Tables	p. 581
A Brief Introduction to MINITAB, Microsoft Excel, and SAS	p. 593
Matrices and Their Application to Regression Analysis	p. 615
Solutions to Selected Odd-Numbered Exercises	p. 625
References	p. 639
Index	p. 641
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