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Consider the following regression estimates (XM3): 1,260 8.22 Linear regression Number of obs F(2, 1257) Prob > F 0.0003 R-squared 0.0125 Root MSE .59124 Robust lwage Coef. Std. Err. t P>|t| [95% Conf. Interval] -.2087896 -.1076566 belavg abvavg _cons .0515497 -4.05 0.000 -.3099225 -.0454376 .0376333 -1.21 0.228 -.1192687 .0283935 1.698296 .0219465 77.38 0.000 1.65524 1.741351 where Iwoge is the natural logarithm of wage, belavg = 1 if the person's looks are rated as below average, and abvavg = 1 if the person's looks are rated as above average.

Consider the following regression estimates (XM3): 1,260 8.22 Linear regression Number of obs F(2, 1257) Prob > F 0.0003 R-squared 0.0125 Root MSE .59124 Robust lwage Coef. Std. Err. t P>|t| [95% Conf. Interval] -.2087896 -.1076566 belavg abvavg _cons .0515497 -4.05 0.000 -.3099225 -.0454376 .0376333 -1.21 0.228 -.1192687 .0283935 1.698296 .0219465 77.38 0.000 1.65524 1.741351 where Iwoge is the natural logarithm of wage, belavg = 1 if the person's looks are rated as below average, and abvavg = 1 if the person's looks are rated as above average.

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
Problem 1P
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Interpret the belavg coefficient. 

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Consider the following regression estimates (XM3): Linear regression Number of obs 1,260 8.22 F(2, 1257) Prob > F 0.0003 R-squared 0.0125 Root MSE .59124 Robust lwage Coef. Std. Err. t P>|t| [95% Conf. Interval] -.2087896 -.3099225 belavg abvavg .0515497 -4.05 0.000 -.1076566 -.0454376 .0376333 -1.21 0.228 -.1192687 .0283935 _cons 1.698296 .0219465 77.38 0.000 1.65524 1.741351 where iwoge is the natural logarithm of wage, belavg = 1 if the person's looks are rated as below average, and abvavg = 1 if the person's looks are rated as above average. Interpret the belovg coefficient. I| || ||| I
Transcribed Image Text:Consider the following regression estimates (XM3): Linear regression Number of obs 1,260 8.22 F(2, 1257) Prob > F 0.0003 R-squared 0.0125 Root MSE .59124 Robust lwage Coef. Std. Err. t P>|t| [95% Conf. Interval] -.2087896 -.3099225 belavg abvavg .0515497 -4.05 0.000 -.1076566 -.0454376 .0376333 -1.21 0.228 -.1192687 .0283935 _cons 1.698296 .0219465 77.38 0.000 1.65524 1.741351 where iwoge is the natural logarithm of wage, belavg = 1 if the person's looks are rated as below average, and abvavg = 1 if the person's looks are rated as above average. Interpret the belovg coefficient. I| || ||| I
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