3. Max Mig. is trying to forecast its maintenance costs for its manufacturing equipment. Data was obtained on 10 of its CNC machines randomly selected from its company database. The company is attempting to predict maintenance costs on 3 possible independent variables: X1 = the age of the machines in years, X2 = hours of required preventative maintenance (PM) last year, and X3 = the presence or absence of complete overhaul last year. The data is below. Create the necessary regression models using Excel and answer the following: a) What is the multiple regression equation for predicting maintenance costs based on all three independent variables. b) According to the model in part a), on average, how much does the presence of an overhaul add to the yearly maintenance costs? c) Determine the regression equation which most accurately predicts the maintenance costs by performing backwards step-wise regression. Validate your final model by using methods demonstrated in class. Make sure to validate your final model by applying hypothesis testing to demonstrate significance of individual parameter estimates (use alpha = 0.05). (Be sure to state what the best estimated regression function is). Y X1 X2 X3 (hours of PM time (presence of (main. Costs) Age (years) in the last year) overhaul) 2050 4 10 980 2 4.3 1330 6.5 O 2100 11.5 950 3 1450 7 1945 8 1900 15 2000 14 890 2