Use excel to corroborate Use of data analysis: use of regression ZANCATEx produc
ID: 3207801 • Letter: U
Question
Use excel to corroborate Use of data analysis: use of regression ZANCATEx produces clothing, specifically men's shirts. This is one of many firms that produce a homogeneous product, and none of them uses advertising to make their product known. By early the December 2016, the manager of this firm must prepare the production plan for the first quarter of that following year. For this, it was necessary to first estimate the possible market prices of the shirts for time. Accordingly, to these estimates, the firm could establish its production volume. The marketing direction estimated three possible scenarios for the next quarter. These prices (per shirt) were: scenario 1 $28; Scenario 2:$36: And, Scenario 3: $48. Using production information and costs, the company wants to estimate its average variable cost using the following quadratic specification AVC at Trimester AVC Q 2014-3 $36.26 300 2014-4 37.33 100 2015-1 27.11 150 2015-2 26.89 250 2015.3 45.10 400 20154 3134 200 2016-1 42.24 350 2016-2 55.13 450 2016-3 61.73 500 It is also known that the monthly total fixed cost ATFC) of ZANCATEx, Inc. amounts to 2,000 per month. It is also known that the market wage is $10 an hour.Explanation / Answer
A)
The estimated regression model is
AVC = 44.479 - 0.142Q + 0.000363Q2
Interpret the intercept: If the production of the product is 0 unit, then we predict the average variable cost of the product is $44.479
Interpret the slope1: If the production of the product increase by 1 unit, then we predict the average variable cost of the product is decerase by $0.142
Interpret the slope2: If the production of the square product increase by 1 unit, then we predict the average variable cost of the product is incerase by $0.000363
B)
(c)
SUMMARY OUTPUT Regression Statistics Multiple R 0.968626 R Square 0.938236 Adjusted R Square 0.917648 Standard Error 3.463289 Observations 9 ANOVA df SS MS F Significance F Regression 2 1093.219 546.6095 45.57218 0.000236 Residual 6 71.96621 11.99437 Total 8 1165.185 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 44.47986 6.483626 6.860337 0.000472 28.615 60.34472 Q -0.14267 0.048198 -2.96009 0.02528 -0.26061 -0.02473 Q2 0.000363 7.89E-05 4.592648 0.003721 0.000169 0.000556Related Questions
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