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In a linear regression problem, we are using “R-squared” to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.

Which of the following option is true?

A. If R Squared increases, this variable is significant.

B. If R Squared decreases, this variable is not significant.

C. Individually R squared cannot tell about variable importance. We can’t say anything about it right now.

D. None of these.

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Solution: C

“R squared” individually can’t tell whether a variable is significant or not because each time when we add a feature, “R squared” can either increase or stay constant. But, it is not true in case of “Adjusted R squared” (increases when features found to be significant).


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