(a). Given in the data, we have values of production for the
past 24 months: the total cost and quantity. If we plot the data on
a graph, we get a scatter-plot. Using regression, our aim is to
find a straight line that satisfies most of the points on the
graph.
To find the best fit line, we use the linear regression method.
First, we find the sum of the x-values.
x_sum =
35+89+56+53+33+38+52+78+54+60+32+18+17+28+56+70+73+44+54+56+79+14+62+39
= 1190
Similarly, y_sum = 1,51,137.4
Next, we find the xy_sum (product of x and y summed for each
month)
xy_sum = (35*3455.5) + (89*17040.82).... = 91,10,108.4
Next, we find the sum of squares of all elements of x and
y.
x_square = 68504
y_square = 975578117.4
N = 24
If equation of line is y = mx + c
then, the value of m is
m = (N * xy_sum) - (x_sum * y_sum) / (N * x_square) - (x_sum *
x_sum)
m = (24 * 9110108.4) - (1190 * 151137.4) / (24 * 68504) - (1190 *
1190)
m = 170.13
The intercept for the best-fit line can be obtained from :
c = (x_square * y_sum ) - (x_sum * xy_sum) / (N * x_square) -
(x_sum * x_sum)
c = (68504 * 151137.4) - (1190 * 9110108.4) / (24 * 68504) - (1190
* 1190)
c = -2138.25
Therefore, the equation of the line is
TC = 170.13 q - 2138.25
(b) Average Total Cost (ATC) = TC/q
ATC = 170.13 - (2138.25/q)
Marginal Cost (MC) = dTC/dq
MC = 170.13
(c). Using the formulae calculated above, we can fill the given
table for all levels of production,
For q = 15,
TC = (170.13*15) - 2138.25 = 413.7
MC = 170.13
ATC = 413.7/15 = 27.58
And so on, for all other values of q.
(d). For q = 20,
ATC = 63.2175
MC = 170.13
The average total cost implies that when 20 boards are being
produced, the cost of production of one board is 63.22. When 15
boards were produced, the cost of production of one board was
27.58.
A marginal cost of 170.13 implies that when the company was
producing 19 snowboards, they were incurring some cost. To increase
the level of production by 1 more, to reach 20, they had to take on
an additional cost of 170.13.
Before q = 12. TC values end up negative, this implies that it is not up to scale, manufacturing at that level of production is not sustainable.
Hope this helped!
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