19. Suppose Q is related to L and K in the following nonlinear way: Q = aLbK c a. How can this nonlinear equation be transformed into a linear form that can be analyzed by using multiple regression analysis? One hundred twenty-three observations are used to obtain the following regression results: b. Which one of the estimated coefficients is/are statistically significant at the 10 percent level of significance? c. How well does this nonlinear model fit the data? d. Write the estimated equation in either the log-linear form or the nonlinear way e. What is the estimated elasticity of L? Of K?

MATLAB: An Introduction with Applications
6th Edition
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Author:Amos Gilat
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19. Suppose Q is related to L and K in the following nonlinear way:

Q = aLbK c

a. How can this nonlinear equation be transformed into a linear form that can be analyzed by using multiple regression analysis?

One hundred twenty-three observations are used to obtain the following regression results:

b. Which one of the estimated coefficients is/are statistically significant at the 10 percent level of significance?

c. How well does this nonlinear model fit the data?

d. Write the estimated equation in either the log-linear form or the nonlinear way

e. What is the estimated elasticity of L? Of K? 

DEPENDENT VARIABLE: Q
OBSERVATIONS: 123
VARIABLE
INTERCEPT
L
K
R-SQUARE
0.7547
PARAMETER
ESTIMATE
5.5215
0.650
0.350
F-RATIO
184.56
STANDARD
ERROR
0.9750
0.2950
0.1450
P-VALUE ON F
0.00001
T-RATIO
5.66
2.20
2.41
P-VALUE
0.0001
0.0295
0.0173
Transcribed Image Text:DEPENDENT VARIABLE: Q OBSERVATIONS: 123 VARIABLE INTERCEPT L K R-SQUARE 0.7547 PARAMETER ESTIMATE 5.5215 0.650 0.350 F-RATIO 184.56 STANDARD ERROR 0.9750 0.2950 0.1450 P-VALUE ON F 0.00001 T-RATIO 5.66 2.20 2.41 P-VALUE 0.0001 0.0295 0.0173
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