Good morning,

I am having trouble beginning this problem. I know I will have to build a model and use analytic solver software in excel, but I am not exactly sure where to start....could you help?

Question

3. Cost modeling. General widget Corporation has collected data on the daily

output and daily production cost of widgets produced at its factory. (Data for

the study are shown in the table below.) The company believes that daily

output (DO) and daily production cost (PC) ought to be linearly related. Thus,

for some numbers a and b:

PC = a + b X DO

Output

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

Day 8

Day 9

Day 10 Production

Cost 5,045

6,127

6,360

6,645

7,220

9,537

9,895

10,175

10,334

10,885 2,542

2,812

2,776

3,164

4,102

4,734

4,238

4,524

4,869

4,321 a. Build a least-squares model to estimate the parameters a and b in the linear

relationship. In other words, minimize the sum of squared differences between

the model's predicted values and the observations. What are the best values of

the parameters a and b for the criterion? What is the minimum value of the

objective function? (Verify your results by using Excel's Regresson Tool.)

b. Suppose, instead, that a better criterion is thought to be minimizing the sum

of absolute deviations between the model's predicted values and the observations.

What are the best values of the parameters a and b for this criterion? What is

the minimum value of the objective function?

c. Suppose, instead, that a better model than the linear model is thought to be

the power function.

PC = a(DO)^b

Using the sum of absolute deviations (as in the previous part), what values of

a and b provide the best fit? What is the minimum value of the objective

function?