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
icon
Related questions
Question

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
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
Expert Solution
steps

Step by step

Solved in 2 steps with 2 images

Blurred answer
Recommended textbooks for you
MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman