Which of the following is true about multi-collinearity? Group of answer choices The P-value reduces significantly, leading to rejection of the null hypothesis. The effect of an independent variable on the dependent variable becomes difficult to isolate. It is measured using a measure called variance inflation factor (VIF). The regression coefficients become clearer and are easier to interpret.

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Mathematics

Kaia Shields

17 January, 20:39

Which of the following is true about multi-collinearity? Group of answer choices The P-value reduces significantly, leading to rejection of the null hypothesis. The effect of an independent variable on the dependent variable becomes difficult to isolate. It is measured using a measure called variance inflation factor (VIF). The regression coefficients become clearer and are easier to interpret.

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Lexus · Answer 1

It is measured using a measure called variance inflation factor (VIF).

Step-by-step explanation:

This is the statement that is true about multi-collinearity. Multi-collinearity is a situation that can sometimes arise when dealing with a multiple regression model. Multicollinearity refers to the occurrence of high intercorrelations among various independent variables. This can often lead to results that are skewed or misleading. The main way in which multi-collinearity can be measured is by using the variance inflation factor (IVF). This allows us to quantify the severity of multi-collinearity.