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Applied regression analysis for business and economics / Terry E. Dielman.

By: Material type: TextTextPublication details: Pacific Grove, CA : Duxbury/Thomson Learning, 2001Edition: 3rd edDescription: viii, 647 p. ill. ; 25 cmISBN:
  • 0534379559
Subject(s): DDC classification:
  • 330/.01/51936 21 D561
Contents:
1. An introduction to regression analysis 2. 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 / using the computer 3. Simple regression analysis using regression analysis 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 4. 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 5. Fitting curves to data introduction / fitting a curvilinear relationship / using the computer 6. 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 assumption that the disturbances are independent / multicollinearity / using the computer 7. Using indicator and interaction variables using and interpreting indicator variables / interaction variables / seasonal effects in time-series regression / using the computer 8. Variable selection introduction to variable selection / all-possible regressions / other variable selection techniques / which variable selection procedure is best? / using the computer 9. 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 / using the computer. (part contents).
List(s) this item appears in: Economics & business administration
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"Data sets for the exercises in this text can be accessed by going to the Web site www.duxbury.com ... "--P. viii.

1. An introduction to regression analysis 2. 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 / using the computer 3. Simple regression analysis using regression analysis 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 4. 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 5. Fitting curves to data introduction / fitting a curvilinear relationship / using the computer 6. 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 assumption that the disturbances are independent / multicollinearity / using the computer 7. Using indicator and interaction variables using and interpreting indicator variables / interaction variables / seasonal effects in time-series regression / using the computer 8. Variable selection introduction to variable selection / all-possible regressions / other variable selection techniques / which variable selection procedure is best? / using the computer 9. 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 / using the computer. (part contents).

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