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The linear regression equation, Y = a + bX, was estimated. The following computer printout was...

The linear regression equation, Y = a + bX, was estimated. The following computer printout was obtained:



Given the above information, the value of the R2 statistic indicates that

0.3066% of the total variation in X is explained by the regression equation.

30.66% of the total variation in X is explained by the regression equation.

0.3066% of the total variation in Y is explained by the regression equation.

30.66% of the total variation in Y is explained by the regression equation.

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Answer #1

The simple way to understand R2 is that R2 tells you how much variance is explained by your model. R2 equal to sum square regression/ sum square of total. It is the ratio between the variance of regression and the total variance of data. The sum square error is the variance left by model, so sum square error plus sum square regression is equal to sum square total, and the R2 equation can be written as 1- sum square error/sum square total

The linear regression equation, Y = a + bX, was estimated . Then if R square has a value of 0.3066 . Then this means that 30.66% of the total variation in Y is explained by the regression equation . In other words , we can say that 30.66 % of total variation in dependent variable (i.e Y ) is basically explained by our independent variable ( i.e X ) .

Hence (D) part is a correct answer

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