Question 19 (10 points) Suppose a researcher is trying to estimate the impact of having a bachelor's degree and gender on wages and has gotten the following regression results from STATA. The correct way to write the estimated regression model is ------------------------------------------------------------------------------ wage | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- bachelor | 5.715977 .0671311 85.15 0.000 5.5844 5.847553 _cons | -11.92882 4.862411 -2.45 0.014 -21.4591 -2.398531 ------------------------------------------------------------------------------ Question 19 options:
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To write the estimated regression model based on the provided STATA output, we need to follow these steps: --- ** Show more…
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The university wanted to estimate the impact of obtaining a bachelors degree as a way to help high school students decide to go to college. They survey 2300 working adults with either a bachelors degree or a high school diploma. In the survey they record their gender (binary, male or female), their age, the average number of hours worked in a week and if their education level. After running a regression they get the following results. Model Parameters (Wage) Source Value Std_Err t P_value Intercept -11330.351 6773.001 -1.673 0.098 Age 433.304 91.943 4.713 <0.0001 Female -18830.264 2588.338 -7.275 <0.0001 Hours 1030.220 125.066 8.237 <0.0001 Bachelors 31859.392 2511.754 12.684 <0.0001 Predict the income of a 34 year old female with a high school diploma that works 40 hours a week. (round to the nearest dollar)
Adi S.
Lucas F.
The following computer output shows an estimated equation. Here; W: Weekly wage. MALE: Gender, takes the value of 1 if the worker is male and 0 if female. EDU: Education Level, EDU1=1 if the worker has no formal educationand 0 otherwise, EDU2=1 if the worker has primary educationand 0 otherwise, EDU3=1 if the worker has secondary and high school degree and 0 otherwise, EDU4=1 if the worker hasbachelor, master, and/or Ph.D. degree and 0 otherwise. EXP: Experience (the number of years being employed). Dependent Variable: W Method: Least Squares Date: 01/02/21. Time: 09:15 Sample: 1 935 Included observations: 935 Variable Coefficient Std. Error t-Statistic Prob. C 508.7969 49.56471 10.26531 0.0000 MALE 486.2831 20.27897 23.97967 0.0000 EDU2 37.93184 35.58875 1.065838 0.2868 EDU3 153.9977 40.51264 3.801225 0.0002 EDU4 251.2214 40.92353 6.138799 0.0000 EXP 10.28997 2.548076 4.038331 0.0001 R-squared 0.461610 Mean dependent var 957.9455 Adjusted R-squared 0.458713 S.D. dependent var 404.3608 S.E. of regression 297.4973 Akaike info criterion 14.23508 Sum squared resid 82220789 Schwarz criterion 14.26615 Log likelihood -6648.902 Hannan-Quinn criter. 14.24693 F-statistic 159.3033 Durbin-Watson stat 0.446774 Prob(F-statistic) 0.000000 a)Write out the estimated wage model below(use 1-digit for decimal): b)Check the statistical significance of EXP, and EDU2 at 5 % level respectively (hypothesis tests) below: c)Interpret the coefficientof determinationbelow: d)Interpret the coefficients of MALE, EDU3, and EXPbelow
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