. reg earnings age educ height weight Number of obs F(4, 17865) Prob > F Rsquared Ad j Iii-squared Root MSE 299.4757 3802.643 367.4161 -12.57531 II II II 0 no I'- . |_|. '4 O IL - O ' 00 O 00 \"I O C) D II II O [0 . db IL U'I Ch 00 (.0 00 rp- 371.6663 4077.71 565.2429 2.620448 Source I SS df MS _____________ +__________________________________ Model | 2.1960e+12 ______ 5 . 4900e+11 Residual | 1 . 0757e+13 17,865 602103714 _____________ +__________________________________ Total I 1.2953e+13 17,869 724863544 earnings I Coefficient Std err t P>Itl _____________ +________________________________________________________________ age I 335.571 18.41505 18.22 0.000 educ I 3940.177 70.16664 _____ 0.000 height I 466.3295 ________ 9.24 0.000 weight I -4.977433 3.876278 -1.28 _____ _Cons I _________ 3352.679 -15.085 0.000 -57146.16 -44003.01 1 Fill in the blanks Complete the missing information in the Stata output above. 2 BER Calculate the standard error of the regression. Hint: this can be written as 8.1 3 8% Calculate the sample variance of earnings. Hint: you could use SER or a sum of squares. 4 Assumptions Do you think this model satisfies assumptions for multiple regression? List 4 of the assumptions and next to each one tell why you do or do not think it is valid. 1) 2) 3) 4) 5 Predict Predict the annual earnings of a 5'8" 27-year old with a 4-year college diploma who weighs 185 pounds. 6 Hypothesis Test Construct and perform a hypothesis test at the a = 0.05 level that workers earn $300 extra per year they get older. Do you reject the null hypothesis? 7 Confidence Interval Construct a 90% confidence interval for the coefficient on weight. Interpret this confidence interval. 8 Specification Can you think of any other variables to include in the model's specification? Why would you want to use those variables