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999			_c15943 _d15943
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005			20200608112244.0
008			000520s2001 caua b 001 0 eng
020			_a0534379559
040			_cDLC
082	0	0	_a330/.01/51936 _221 _bD561
100	1		_aDielman, Terry E.,
245	1	0	_aApplied regression analysis for business and economics / _cTerry E. Dielman.
250			_a3rd ed.
260			_aPacific Grove, CA : _bDuxbury/Thomson Learning, _c2001
300			_aviii, 647 p. _bill. ; _c25 cm.
500			_a"Data sets for the exercises in this text can be accessed by going to the Web site www.duxbury.com ... "--P. viii.
650		0	_aEconomics _xStatistical methods.
650		0	_aCommercial statistics.
650		0	_aRegression analysis.
942			_cBK
505	0		_a1. 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).