Observations are taken on net revenue from sales of a certain plasma TV at 50 retail outlets.
The regression model was
y = net revenue (thousands of dollars),
x1 = shipping cost (dollars per unit),
x2 = expenditures on print advertising (thousands of dollars),
x3 = expenditures on electronic media ads (thousands),
x4 = rebate rate (percent of retail price).
A) Write the fitted regression equation
B) Interpret each coefficient
C) Would the intercept be likely to have meaning in this regression
D) Use the fitted equation to make a prediction for NetRevenue when ShipCost = 10, PrintAds = 50, WebAds = 40, and Rebate% = 15.
A) The fitted regression equation should be as follows:
y = 4.306 -0.082x1+ 2.265x2 + 2.498x3 + 16.697x4
B) 4.306 is the intercept, which implies that when shipping cost = 0, combined expenditures = 0, and rebate rate = 0, we have a net revenue of 4.306 (or $4,306 since the units are thousands of dollars). You can think of this as a baseline revenue.
The full solution includes step-by-step explanations with the fitted regression equation, and how to interpret the regression results as well as the final prediction.