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?
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
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
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](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fdd7b5ed6-a0e5-4e72-852f-588ea0c73f46%2Ff6599958-51a2-4e29-ba52-979b62636f5d%2F4n7l2kn_processed.png&w=3840&q=75)
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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