A researcher developed the following multiple regression model to explain the variation in hours worked by married women.

H = ?0 + ?1X1 + ?2X2 + ?3X3 + ?4X4 + ?

Where, H = hours worked per month, X1 = age, X2 = education level, X3 = experience, X4 = husband’s wage, ?s = the parameters to be estimate, and ? = 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. (8 pts.) Test the statistical significance of the coefficient estimate of each explanatory variable at 5% significance level.

b. (3 pts.) Test the statistical significance of the overall model.

c. (2 pts.) Write the *estimated*regression equation using the coefficient estimates** **givenabove.

d. (2 pts.) Do the *experience* and the *husband’s wage* variables have the expected signs?

e. (2 pts.) If the education level increases by 1and all the other variables do not change, what will happen to the number of hours worked according to this model?

f. (2 pts.)What percent of the variation in hours worked is explained by this model