Marginal effect of taking econometrics

Reference no: EM1376115

An Economics section at a large university keeps track of its majors begninnig salaries. We address the question of the value of taking econometrics, based on last year's crop of 50 majors. Let SAL=$ salary, GPA = grade point average on a 4.0 scale, METRICS=1 if student took
econometrics, METRICS = 0 otherwise, SEX=1 if student is a female, otherwise =0.

Consider the following regression
SAL = B1 + B2GPA + B3METRICS + B4METRICS * GPA + et

(a) Based on table 1, what is the marginal effect (benefit) of taking econometrics?

(b) What is the predicted wage difference between a student who took econometrics and one who did not, given that their GPA =3.0?
Consider another regression

SAL = B1+B2GPA+B3METRICS+B4SEX+B5METRICS*SEX+et

(d) Does the model suggest salary difference among gender? Justify your answer

(e) Is the value of econometric the same for men and women? Justify your answer.

Source | SS df MS Number of obs = 50
-------------+------------------------------ F( 3, 46) = 45.59
Model | 275083851 3 91694616.9 Prob > F = 0.0000
Residual | 92520533 46 2011315.94 R-squared = 0.7483
-------------+------------------------------ Adj R-squared = 0.7319
Total | 367604384 49 7502130.28 Root MSE = 1418.2

------------------------------------------------------------------------------
salary | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gpa | 1918.108 396.7813 4.83 0.000 1119.428 2716.787
metrics | 8440.214 2399.488 3.52 0.001 3610.296 13270.13
metricsgpa | -1197.261 828.1421 -1.45 0.155 -2864.224 469.7027
_cons | 23379.25 1207.748 19.36 0.000 20948.18 25810.32

Reference no: EM1376115

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