A multiple linear regression model is fit to a data set of 20 observations, giving =7+8X1+6X2+14X3....

 

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A multiple linear regression model is fit to a data set of 20 observations, giving =7+8X1+6X2+14X3. a.) If SSE 31, calculate MSE= b.) If in addition, the estimated standard error of is 3.9, calculate the standard error of prediction of a new response at regressor values X =1, X2 = 5, X = 2. (Ynew-new) = c.) Calculate a 99% prediction interval for a new observation at X = 1, X2 = 5, X3 = 2 0.0 A multiple regression model Y = Bo+ BX1+ BX2 + BaXs+e is fit to a data set with 26 observations. If a 95% confidence interval for the mean response at X =2, X2=-4, X3 7 is (-1, 1), and a 95% prediction interval for a new observation at X =2, X2 -4, Xs 7 is (-2, 2), then to three decimal places the mean square error (MSE) equals A multiple linear regression model is fit to a data set of 20 observations, giving =7+8X1+6X2+14X3. a.) If SSE 31, calculate MSE= b.) If in addition, the estimated standard error of is 3.9, calculate the standard error of prediction of a new response at regressor values X =1, X2 = 5, X = 2. (Ynew-new) = c.) Calculate a 99% prediction interval for a new observation at X = 1, X2 = 5, X3 = 2 0.0 A multiple regression model Y = Bo+ BX1+ BX2 + BaXs+e is fit to a data set with 26 observations. If a 95% confidence interval for the mean response at X =2, X2=-4, X3 7 is (-1, 1), and a 95% prediction interval for a new observation at X =2, X2 -4, Xs 7 is (-2, 2), then to three decimal places the mean square error (MSE) equals