Multicollienarity (check all that apply): Question options: arises when some of the explanatory variables are highly...

Multicollienarity (check all that apply):

Question options:

	arises when some of the explanatory variables are highly correlated either with each other or with a combination of some of the rest of the explanatory variables.
	can be best detected by checking the values of the so-called variance inflation factors (VIFs).
	doesn't need to be corrected for if the only goal of the analysis is to use the regression equation for predictions of the response variable, because the regression standard error is not affected by multicollinearity.
	is a potentially serious problem that can arise in multiple regression.
	should be necessarily corrected for if the goals of the regression analysis include interpretation of the coefficient estimates in front of the explanatory variables.

Expert Solution

First we'll see multicollinearity in short:

Cause: High correlation among two or more independent variable.

Effect: Distorts the standard error of coefficient, this will increase the probability of concluding that the variable is insignificant when In fact it is significant ie. Increasing type 2 error.

Detection: High R square, F-test says significant but t-test says no individual variable is significant.High VIF

Correction: Correlation matrix, Factor analysis

Now we can answer the asked question:

Therefore the options that follow are:

arises when some of the explanatory variables are highly correlated either with each other or with a combination of some of the rest of the explanatory variables.

can be best detected by checking the values of the so-called variance inflation factors (VIFs).

is a potentially serious problem that can arise in multiple regression.

should be necessarily corrected for if the goals of the regression analysis include interpretation of the coefficient estimates in front of the explanatory variables.

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orchestra answered 3 years ago

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