In: Statistics and Probability
Consider the following data on the quantity of production and its cost (in dollars) for a particular customized component, for each of 10 weeks.
NumberProduced Cost
16 207.17
6 90.93
18 227.22
10 153.61
13 155.97
3 68.24
9 112.00
16 185.77
20 260.48
5 80.62
a. Find the usual statistical measure for the strength of the association between cost and number produced. Please give both the name and numeric value for your answer.
Name: |
Numeric Value: |
b. Find, to the penny, the estimated cost of producing one additional component (this is the “variable cost” or the “variable cost of production”).
Variable Cost: |
c. How well has this variable cost (from part b) been estimated? Please answer by providing a 95% confidence interval for the true variable cost of this production process.
Confidence Interval: |
d. Estimate, to the penny, the cost associated with producing 12 units in a week, assuming the same process from which the data were obtained.
Cost to produce 12 units: |
a) To test the measure of association between cost and number produced, pearson correlation should be used.
It can be calculated using excel .
Following are the steps:
Step 1: put the data into the spreadsheet
Step 2: Go to data --> data analysis --> correlation
Step 3: Select the data and get the output
a.
Name: Pearsons Correlation
Numerical measure = 0.9839
b. To estimate the cost of producing one additional component, we need to perform the regression analysis and get the regression equation.
Step 1: go to data --> data analysis --> regression
Step 2: select the data ( choose cost as y variable, number produced as x variable) and get the output
The regression equation: y = 26.227 + 11.03x
Hence, the cost producing one additional component is $ 11.03
c) The 95% percent confidence interval is given in the regression output
The confidence interval is (9.4014 , 12.662)
So, the variable cost is part b is well estimated as $ 11.03 comes between the confidence limits
d) The cost to produce 12 units
Cost (y) = 26.227 + 11.03 * 12= $ 158.587