How does multicollinearity affect the stability of coefficient estimates in regression analysis? It makes the estimates more stable It makes the estimates less stable It has no effect on the stability of estimates It depends on the sample size

Which of the following is NOT a method to address multicollinearity in regression analysis? Removing one of the correlated predictors Regularization techniques Transforming the predictors Increasing the number of predictors

Imagine you regressed earnings of individuals on a constant (intercept), a dummy variable (‘male’) which takes on the value 1 for males and is 0 otherwise, and another dummy variable (‘female’) which takes on the value 1 for females and 0 otherwise. Because females typically earn less than males, you would expect ________. a. none of the OLS estimators to exist because there is perfect multicollinearity b. both coefficients to be the same distance from the constant, one above and the other below c. the coefficient for ‘male’ to have a positive sign, and for ‘female’ a negative sign d. the coefficient for ‘male’ to have a negative sign, and for ‘female’ a positive sign

4.5 – One potential issue the analyst faces when using multiple linear regression analysis isthe multicollinearity of the independent variables. Verify whether or not multicollinearity existsamong the independent variables. This is done by examining the correlation between each ofthe independent variables. Think of this as a correlation matrix (must be included in yourdocument) which can be easily performed in Excel using the “Data Analysis” tool pack. If theindependent variables are highly correlated, then the analyst is unable to isolate the effect ofeach independent variable on the dependent variable. Thus, analysis essentially becomespointless

Which of the following is a factor that affects the stability of the slope

Because it accounts for more variables that can influence the dependent variable, the multiple regression model may help us reduce the chance of having biased estimates of the regression coefficientSelect one:TrueFalse