Like many fast-food restaurant chains, Burger King (BK) provides data on the nutrition content of its menu items on its website. Here€™s a multiple regression predicting calories for Burger King foods from Protein content (g), Total Fat (g), Carbohydrate (g), and Sodium (mg) per serving.

a) Do you think this model would do a good job of predicting calories for a new BK menu item? Why or why not?
b) The mean of Calories is 455.5 with a standard deviation of 217.5. Discuss what the value of s in the regression means about how well the model fits the data.
c) Does the R2 value of 100.0% mean that the residuals are all actually equal to zero?

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Dependent variable is: Calories R-squared:: 100.0% R-squared [adjusted ]"100.0% s3. 140 with 31-5 26 degrees of freedom Source Regression 1419311 Residual Sum of Squares df Mean Square F-ratio 35994 4 354828 26 256.307 9.85796 Variable Coeff Intercept 6.53412 2.425 Protein Total fat 9.14121 0.0779 Carbs Na/Serv. -0.69155 0.2970 SE(Coeff) t ratio P-value 2.690 0.0122 0.0001 <0.0001 0.0001 0.0279 3.83855 0.0859 44.7 3.94033 0.0338 -2.33