A researcher developed the following multiple regression model to explain the variation in hours worked by married women.
H = β0 + β1X1 + β2X2 + β3X3 + β4X4 + ε
Where, H = hours worked per month, X1 = age, X2 = education level, X3 = experience, X4 = husband's wage, βs = the parameters to be estimate, and ε = the error term.
All the explanatory variables (age, education level, experience, and husband's wage) are expected to have negative impact on hours of work.
The researcher collected data on H and Xs for a random sample of 428 working women in a given geographical area. Upon estimation of the model, the researcher obtained the following regression output.
Explanatory Variable Coefficient Estimate Standard Error of Estimate
Constant 1817.334 296.445
X1 -16.456 5.365
X2 -38.363 16.067
X3 49.487 13.734
X4 -66.505 12.842
Dependent Variable: Hours
Observation (n) = 428
SSR = 691.8015
SST = 1061.8015
F-ratio = 16.806
a. Test the statistical significance of the coefficient estimate of each explanatory variable at 5% significance level.