Multiple linear regression b) Til151, Ci2= 63, > X;1X¿2789,Yi1760i=1i=1i=1i=11313131321650,Xi2Yi9040,x = 1887, x = 331Xi1Yii=1i=1i=1i=1(a)sary inverse matrix, but should show...

Multiple linear regression

b)

Extracted text: Suppose we want to understand the relationship between Fat content (X1) and Saturated Fat content (X2) on the Calories of 13 hot dog brands. We wish to build a multiple linear model for these data, based on the below summary values: 13 13 13 13 > Til 151, Ci2 = 63, > X;1X¿2 789, Yi 1760 i=1 i=1 i=1 i=1 13 13 13 13 21650, Xi2Yi 9040, x = 1887, x = 331 Xi1Yi i=1 i=1 i=1 i=1 (a) sary inverse matrix, but should show all other steps in your work. Estimate the fitted regression coefficients. You may use software to find the neces- (b) Interpret the coefficient of Fat in the context of the data. (c) of squares for this model is 313.6. Find a 95% confidence interval for the slope of Saturated Fat, if the residual sum (d) simple linear regression, we derived the sampling distribution to be used in building pre- diction intervals for a predicted response at the value X = x* by considering instead the sampling distribut and distribution for the error in a prediction based on a multiple linear model involving predictors. This question is independent of the previous questions. Recall that in of Y* – ĝ*, the error in our prediction. Determine the mean, variance,