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Applied regression analysis for business and economics

Author: Terry E Dielman
Publisher: Pacific Grove, CA : Duxbury/Thomson Learning, ©2001.
Edition/Format:   Print book : English : 3rd edView all editions and formats
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This text covers 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  Read more...

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Additional Physical Format: Online version:
Dielman, Terry E., 1952-
Applied regression analysis for business and economics.
Pacific Grove, CA : Duxbury/Thomson Learning, ©2001
(OCoLC)761090441
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Terry E Dielman
ISBN: 0534379559 9780534379551
OCLC Number: 44118027
Notes: "Data sets for the exercises in this text can be accessed by going to the Web site www.duxbury.com ..."--Page viii.
Description: viii, 647 pages : illustrations ; 25 cm
Contents: Review of Basic Statistical Concepts --
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 --
Using the Computer --
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 --
Using the Computer --
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 --
Lagged Variables as Explanatory Variables in Time-Series Regression --
Using the Computer --
Fitting Curves to Data --
Fitting a Curvilinear Relationship --
Using the Computer --
Assessing the Assumptions of the Regression Model --
Assumptions of the Multiple Linear Regression Model --
The Regression Residuals --
Assessing the Assumption That the Relationship Is Linear.
Responsibility: Terry E. Dielman.
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