Cereal ratings: 3 top variables

Suppose you have the following dataset*, which is a list of 80 cereals, containing the following fields:

• mfr: Manufacturer of cereal

• A = American Home Food Products

• G = General Mills

• K = Kelloggs

• N = Nabisco

• P = Post

• Q = Quaker Oats

• R = Ralston Purina

• type:

• cold

• hot

• calories: calories per serving

• protein: grams of protein per serving

• fat: grams of fat per serving

• sodium: milligrams of sodium

• fiber: grams of dietary fiber

• carbs: grams of complex carbohydrates

• sugars: grams of sugars

• potass: milligrams of potassium

• vitamins: vitamins and minerals - 0, 25, or 100, indicating the typical percentage of FDA recommended

• shelf: display shelf (1, 2, or 3, counting from the floor)

• weight: weight in ounces of one serving

• cups: number of cups in one serving

• rating: a rating of the cereals (Possibly from Consumer Reports?)

Using this data, can you determine the best 3 independent variables that help determine cereal rating?

*Dataset source