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.

a. Test the statistical significance of the coefficient estimate of each explanatory variable at 5% significance level.
b. Test the statistical significance of the overall model.
c. Write the estimated regression equation using the coefficient estimates given above.
d. Do the experience and the husband€™s wage variables have the expected signs?
e. If the education level increases by 1 and all the other variables do not change, what will happen to the number of hours worked according to this model?
f. What percent of the variation in hours worked is explained by this model?

Explanatory Variable Coefficient Estimate Standard Error of Estimate Constant X1 X2 X3 X4 1817.334 -16.456 38.363 49.487 66.505 296.445 5.365 16.067 13.734 12.842 Dependent Variable: Hours Observation (n) 428 SSR 691.8015 SST 1061.8015 F-ratio 16.806