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Analysis of Regression Models and Hypothesis Testing in Predicting Dependent Variables

Explores regression models and hypothesis testing for predictive analysis.

Amelia Ward
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1Analysis of Regression Models and Hypothesis Testing in PredictingDependent Variables578Assignment-5 (Chs. 13 and 14)-solutions:Due bymidnight of Sunday,December2nd, 2012: drop box 4): 70 pointsTrue/False(One point each)Chapter 131.Thestandard error of the estimate (standard error) is the estimated standarddeviation of the distribution of the independent variable (X).FALSEit is the estimate of the standard deviation of the error term2.In a simple linear regression model, thecoefficient of determinationonlyindicates the strength of the relationship between independent and dependentvariable, butdoes not showwhether the relationship is positive or negative.TRUER2is greaterthan or equal to 0, no negative3.When usingsimple regression analysis, if there is a strong correlation betweenthe independent and dependent variable, then we can conclude that an increase inthe value of the independent variable causes an increase in the value of thedependent variable.FALSEthe strong correlation could be negative4.The error term is the difference between an individual value of the dependentvariable and the corresponding mean value of the dependent variable.FALSEit is the difference between an individual value of thedependentvariable and the corresponding predicted value (not the mean value) :residual and error term are the same thing5.In bi-variate regression the Coefficient of Determination is always equal to thesquare of the correlation coefficient.TRUE

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