A Complete Specification of the Asymptotic Variance Assignment

A Complete Specification of the Asymptotic Variance - Assignment Example The le contains 6,808 observations for individuals living in Ontario. b) Estimate two human capital earnings models, one for men and one for women. Use wage as the dependent variable and include exp, exp2, Educ, not grad, hs grad somepse, uni1, uni2, and marras independent variables. Discuss the goodness of the of the two equations. From the regression table, we observe that the F-computed is 257.6> 1.88260439 (F-critical), we thus reject the null hypothesis that all regression coefficients are equal to zero. This shows that F-test is significant indicating that the observed R-squared is reliable, and is not a spurious result of oddities in the data set. Also, it shows that the proposed relationship between the response variable and the set of predictors is statistically reliable, and can be useful when the research objective is either prediction or explanation. From the regression table, we observe that the F-computed is 356.47> 1.88266598 (F-critical), we thus reject the null hypothesis that all regression coefficients are equal to zero. This shows that F-test is significant indicating that the observed R-squared is reliable, and is not a spurious result of oddities in the data set. Also, it shows that the proposed relationship between the response variable and the set of predictors is statistically reliable, and can be useful when the research objective is either prediction or explanation. c) Interpret the results of the test of overall significance for each equation. Do not forget to state the decision rule for the test, the level of significance you are using, and the critical value of the test statistic. The p-value for the overall model is 0.0000, a value less than 5%, we thus reject the null hypothesis. The null hypothesis states that the coefficient is equal to zero (no effect). We, therefore, conclude that the model is appropriate and that there is the significant effect on the dependent variable (sewage) by the independent variables.