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Consider the following regression estimates (FN3) Linear regression income hours _cons Robust Coef. Std. Err. 18.91906 1.481248 281.4618 34.36264 Number of obs F(1, 498) Prob > F R-squared Root MSE t P>|t| 12.77 0.000 8.19 0.000 500 163.13 0.0000 0.2880 593.03 [95% Conf. Interval] 16.0088 213.9482 21.82933 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 If we would run a regression of income on days with the same data as above what would be the t-statistic of the days coefficient?

Consider the following regression estimates (FN3) Linear regression income hours _cons Robust Coef. Std. Err. 18.91906 1.481248 281.4618 34.36264 Number of obs F(1, 498) Prob > F R-squared Root MSE t P>|t| 12.77 0.000 8.19 0.000 500 163.13 0.0000 0.2880 593.03 [95% Conf. Interval] 16.0088 213.9482 21.82933 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 If we would run a regression of income on days with the same data as above what would be the t-statistic of the days coefficient?

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
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Consider the following regression estimates (FN3) Linear regression income hours _cons Robust Coef. Std. Err. 18.91906 281.4618 1.481248 34.36264 t Number of obs F(1, 498) Prob > F R-squared Root MSE P>|t| 12.77 0.000 8.19 0.000 |||||||||| = = = 500 163.13 0.0000 0.2880 593.03 [95% Conf. Intervall 16.0088 213.9482 21.82933 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 t-statistic of the days coefficient?
Transcribed Image Text:Consider the following regression estimates (FN3) Linear regression income hours _cons Robust Coef. Std. Err. 18.91906 281.4618 1.481248 34.36264 t Number of obs F(1, 498) Prob > F R-squared Root MSE P>|t| 12.77 0.000 8.19 0.000 |||||||||| = = = 500 163.13 0.0000 0.2880 593.03 [95% Conf. Intervall 16.0088 213.9482 21.82933 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 t-statistic of the days coefficient?
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