The table shows a regression of average hourly earnings (wage) on education (educ), age (age) and the gender dummy (woman). Discuss briefly the results. How do you interpret the intercept and the slope coefficients on these three variables? SourcedfMSNumber of obs4,118F(3, 4114)547.19Model43529.844814509.9483Prob > F0.0000Residual4,114R-squaredAdj R-squared109091.59526.51715970.28520.2847Total152621.444,117 37.0710323Root MSE5.1495wageCoef.Std. Err.tP>|t|[95% Conf. Interval]educ1.127698.034112333.060.0001.0608191.194576age.1195645.007162916.690.000.1055212.1336077woman-2.521433.1606725-15.690.000-2.836438-2.206428Cons-8.213757.5272217-15.580.000-9.247396-7.180117

Consider the provided data that shows regression analysis of hourly earning wage depends on the…

Source df MS Number of obs 4,118 F(3, 4114) 547.19 Model 43529.8448 14509.9483 Prob > F 0.0000 Residual R-squared Adj R-squared 109091.595 4,114 26.5171597 0.2852 0.2847 Total 152621.44 4,117 37.0710323 Root MSE 5.1495 t P>|t| wage Coef. Std. Err. [95% Conf. Interval] educ 1.127698 .0341123 33.06 0.000 1.060819 1.194576 age .1195645 .0071629 16.69 0.000 .1055212 .1336077 woman -2.521433 .1606725 -15.69 0.000 -2.836438 -2.206428 cons -8.213757 .5272217 -15.58 0.000 -9.247396 -7.180117

Source df MS Number of obs 4,118 F(3, 4114) 547.19 Model 43529.8448 14509.9483 Prob > F 0.0000 Residual R-squared Adj R-squared 109091.595 4,114 26.5171597 0.2852 0.2847 Total 152621.44 4,117 37.0710323 Root MSE 5.1495 t P>|t| wage Coef. Std. Err. [95% Conf. Interval] educ 1.127698 .0341123 33.06 0.000 1.060819 1.194576 age .1195645 .0071629 16.69 0.000 .1055212 .1336077 woman -2.521433 .1606725 -15.69 0.000 -2.836438 -2.206428 cons -8.213757 .5272217 -15.58 0.000 -9.247396 -7.180117

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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