Making food the world loves Jeff Harmening, the CEO of General Mills, strives to achieve their slogan of “making food the world loves.” He first collected data on his dependent variable of consumer ratings from 0 (low) to 100 (high) for all cereal brands. He then created an indicator variable generalmills to compare cereals produced by General Mills to cereals produced by competitors. He also collected data on the nutrition of each, including the amount of calories, fiber, and sugars. Below is the correlation matrix of the variables:

0.065 L =65 cL.

Jeff then ran the following regression of ratings on the indicator of generalmills and the nutritional variables of calories, fiber, and sugars:

Calculate a 95% prediction interval for the wine quality in a new batch of Camille’s red wine with a fixed acidity of 10, a residual sugar of 2.5, a density of .995, and an alcohol content of 11.

8 kL = 8000 L  

Which of the following most likely explains why the intercept coefficient is a negative number?

7.5 L = 75000 mL  

Which of the following is a correct interpretation of the coefficient on generalmills in the multivariate regression context?

Based on the correlation matrix, is multicollinearity likely an issue?

Based on the regression output, which of the following is a valid justification for why the coefficients on max and min resolution are negative?