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Cost Modeling. General Widget Corporation has collected data

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 × DO

Output         Production Cost

Day 1  5,045                2,542

Day 2  6,127               2,812

Day 3  6,360                  2,776

Day 4  6,645                 3,164

Day 5  7,220                 4,102

Day 6   9,537                4,734

Day 7   9,895              4,238

Day 8  10,175               4,524

Day 9 10,334 4,869

Day 10 10,855 4,421

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 this criterion? What is the minimum value of the objective function? (Verify your results by using Excel's Regression tool.) 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? 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?

If I can have this by 10 am that would be GREAT!

Thanks:)

Hi,

I'll be happy to help you with these statistics questions. I'll have the solutions posted for you as soon as possible.

Thanks,

Ryan
Hi Misty,

Here are the solutions:

Regression