Question

The following computer printout estimated overhead costs using multiple regression:

t for H(0)                                           Std. error

Parameter        Estimate          Parameter = 0   Pr > t               of parameter

Intercept          1000     1.96                             0.0250                     510.204

Setup hours     35                     81.96                           0.0001                         0.305

# of parts         80                     9.50                             0.0001                        10.527

R Square (R2)                         0.95

Standard Error (Se)      75.00

Observations                          158

During the year the company used 900 setup hours and 500 parts.

A) Refer to Figure 3-3. The degrees of freedom for the model is?

B) Refer to Figure 3-3. The model being measured is?

C) What is the predicted overhead cost?

D) The coefficient of determination in this model tells us that?

Answer 1

(A) degree of freedom = sample size - number of independent variable = 158-2 = 156 because there are only two independent variables

(B) The model being measure is multiple regresion model.

(C) Predicted overhead cost = 1000 + 35*setup hours + 80*(# of parts)

it is given that 900 setup hours used and 500 parts used

setting the value in the regression equation, we get

Predicted overhead cost = 1000 + 35*900 + 80*500 = 1000+31500+40000 = 72500

(D) R squared value is 0.95 which means that the 95% variation in the predicted overhead cost can be explained by setup hours and number of parts.