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Nafraz Najeemudeen

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  1. QUESTION 1 - Which one of the statements below is NOT true? a) Multicollinearity in multiple regression means that variables correlate with each other strongly. b) Multiple regression expresses the effect of one specific X on Y, while controlling for other Xs. c) A multiple regression analysis with 4 variables requires a data set of roughly 30 observations. QUESTION 2 - Which one of the statements below is NOT true? a) Homoscedasticity is a desired characteristic of OLS regression functions. b) In OLS regression, you may use a nominal (preferably dummy) variable as a dependent variable. c) An R squared of 0.9 tells us that 90% of the variance is explained by the regression function. QUESTION 3 - Which one of the statements below is NOT true? a) Homoscedasticity is a desired characteristic of OLS regression functions. b) In OLS regression, you may use a nominal (preferably dummy) variable as a dependent variable. c) An R squared of 0.9 tells us that 90% of the variance is explained by the regression function.
  2. QUESTION 1 - Which one of the statements below is NOT true? a) Multicollinearity in multiple regression means that variables correlate with each other strongly. b) Multiple regression expresses the effect of one specific X on Y, while controlling for other Xs. c) A multiple regression analysis with 4 variables requires a data set of roughly 30 observations. QUESTION 2 - Which one of the statements below is NOT true? a) Homoscedasticity is a desired characteristic of OLS regression functions. b) In OLS regression, you may use a nominal (preferably dummy) variable as a dependent variable. c) An R squared of 0.9 tells us that 90% of the variance is explained by the regression function. QUESTION 3 - Which one of the statements below is NOT true? a) Homoscedasticity is a desired characteristic of OLS regression functions. b) In OLS regression, you may use a nominal (preferably dummy) variable as a dependent variable. c) An R squared of 0.9 tells us that 90% of the variance is explained by the regression function.
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