Consider the following regression estimates (FN3) Linear regression Number of obs F(1, 498) 500 163.13 Prob > F = 0.0000 R-squared 0.2880 Root MSE 593.03 Robust income Coef. Std. Err. t P>|t| [95% Conf. Interval] hours 18.91906 12.77 0.000 16.0088 21.82933 1.481248 34.36264 _cons 281.4618 8.19 0.000 213.9482 348.9754 where income is weekly income in NZ$ and hours is working hours per week. There is another variable called days which is equal to hours/8. For example, if a person would have worked 40 hours per week, then their value for days would be 5 (40/8). If we would run a regression of income on days with the same data as above, what would be the value of the days coefficient? O a. 2.36 b-94.60 OC 26.92 d-151 32
Consider the following regression estimates (FN3) Linear regression Number of obs F(1, 498) 500 163.13 Prob > F = 0.0000 R-squared 0.2880 Root MSE 593.03 Robust income Coef. Std. Err. t P>|t| [95% Conf. Interval] hours 18.91906 12.77 0.000 16.0088 21.82933 1.481248 34.36264 _cons 281.4618 8.19 0.000 213.9482 348.9754 where income is weekly income in NZ$ and hours is working hours per week. There is another variable called days which is equal to hours/8. For example, if a person would have worked 40 hours per week, then their value for days would be 5 (40/8). If we would run a regression of income on days with the same data as above, what would be the value of the days coefficient? O a. 2.36 b-94.60 OC 26.92 d-151 32
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Hi just need answer to this question thanks.
![Consider the following regression estimates (FN3)
Linear regression
Number of obs
F(1, 498)
500
163.13
Prob > F
0.0000
R-squared
=
0.2880
Root MSE
593.03
Robust
income
Coef. Std. Err.
t
P>|t|
[95% Conf. Interval]
16.0088
21.82933
hours
_Cons
18.91906
281.4618
1.481248
34.36264
12.77
8.19
0.000
0.000
213.9482
348.9754
where income is weekly income in NZ$ and hours is working hours per week.
There is another variable called days which is equal to hours/8. For example, if a person would have worked 40 hours per week, then their value for days would be 5 (40/8).
If we would run a regression of income on days with the same data as above, what would be the value of the days coefficient?
O a. 2.36
b.94.60
OC 26.92
O d. 151.32](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F909b168f-c772-43f0-b38c-6f8b46c0e487%2F0826a0c5-deec-4099-9ad4-f4d1d7153fe4%2F9bk0e_processed.png&w=3840&q=75)
Transcribed Image Text:Consider the following regression estimates (FN3)
Linear regression
Number of obs
F(1, 498)
500
163.13
Prob > F
0.0000
R-squared
=
0.2880
Root MSE
593.03
Robust
income
Coef. Std. Err.
t
P>|t|
[95% Conf. Interval]
16.0088
21.82933
hours
_Cons
18.91906
281.4618
1.481248
34.36264
12.77
8.19
0.000
0.000
213.9482
348.9754
where income is weekly income in NZ$ and hours is working hours per week.
There is another variable called days which is equal to hours/8. For example, if a person would have worked 40 hours per week, then their value for days would be 5 (40/8).
If we would run a regression of income on days with the same data as above, what would be the value of the days coefficient?
O a. 2.36
b.94.60
OC 26.92
O d. 151.32
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