Question

A medical researcher was interested in examining what factors influenced patient’s scores in a fitness test. He ran a multiple linear regression, which included four predictors (hours spent taking part in physical activity per day, calories consumed per day, BMI, and hours spent sitting per day). His model had a R2 of .665, an adjusted R2 of .661, an F-statistic of 112.56 (p < 001). How would you interpret his findings?

It is not a significant model.

It becomes difficult to assess the individual importance of predictors and it increases the standard errors of the b coefficients making them unreliable.

It is a significant model where the four predictors account for 112% of the variance in the patient’s scores in the fitness test.

It is an significant model where the four predictors account for 66.1% of the variance in the patient’s scores in the fitness test.

Answer 1

It is an significant model where the four predictors account for 66.1% of the variance in the patient’s scores in the fitness test.

Because F Statistics P value indicate the overall model significant.And from the P value we observe that our model is significant. Because P value is less than Level of Significance.

And R square Indicate the percent of variation explain by all independent variable to the our response (Dependent) Variable.