Table below describes short-run AVC (Average Variable Cost) function in the form
ID: 1107130 • Letter: T
Question
Table below describes short-run AVC (Average Variable Cost) function in the form: AVC = a + bQ + cQ2
Dep. Var.: AVC
R-Square
F-Ratio
P-value on F
Obs.: 43
0.6316
55.91
0.0001
Variable
Para. Est.
Std. Err.
T-Ratio
P-value
Intercept
665.124
138.568
4.80
0.0002
Q
-0.16458
0.09738
-1.69
0.0925
Q2
0.00079
0.00028
2.82
0.0001
1. Based on the cost analysis, is the sign for EACH parameter estimates correct?
2. Is EACH of them statistically significant at the 2% level of significance?
3. Explain.
Dep. Var.: AVC
R-Square
F-Ratio
P-value on F
Obs.: 43
0.6316
55.91
0.0001
Variable
Para. Est.
Std. Err.
T-Ratio
P-value
Intercept
665.124
138.568
4.80
0.0002
Q
-0.16458
0.09738
-1.69
0.0925
Q2
0.00079
0.00028
2.82
0.0001
Explanation / Answer
1. Yes, because with the increase in quantity produced the average variable cost would reduce. So the parameter Q is in negative sign and q^2 is in positive sign as square of the Q is positive.
2. Intercept and Q^2 are statistically significant as the P-value is less than 0.02 where as Q is statistically non- significant as the P-value is greater than 0.02.
3. The equation or model is able to explain 63.16 percent variation in AVC and the P-value is less than 0.01, it is statistically significant at 99% significance level.
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