Your Best Model. Work with the Nutrition data sets for Exercises 41 and 42.
Question 13.143
nutrition
41. Use technology to apply the Strategy for Building a Multiple Regression Model, using level of significance , for predicting the number of calories, with the following -variables: protein, fat, saturated fat, cholesterol, carbohydrates, calcium, phosphorous, iron, potassium, sodium, thiamin, niacin, and ascorbic acid.
13.3.41
The standard error in the estimate for the final model is . That is, using the multiple regression equation given above, the size of the typical prediction error will be about 16.7233 calories. The adjusted coefficient of variation is . In other words, 99.91% of the variation in calories is accounted for by this multiple regression equation.