1. STATA: Use Stata and theGSS 2018 dataset to predict number of hours worked (hrs1) with age (age) and education (educ)
   1. Find the prediction equation.
   2. Interpret the slopes in the prediction equation.
   3. Find and interpret R2.

graph matrix hrs1 age educ 50 100 - 100 number of hours 50 worked last week 100 50 age of respondent - 20 highest year of 10 school completed 50 100 0 10 20 regress hrs1 age educ Source | SS df MS Number of obs 1, 373 F(2, 1370) 5.23 Model 2178 . 82198 2 1089.41099 Prob > F 0. 0055 Residual 285343. 346 1, 370 208. 279814 R-squared II 0. 0076 Adj R-squared 0. 0061 Total | 287522.168 1, 372 209.564262 Root MS 14.432 hrs1 | Coefficient Std. err. t P>It| [95% conf. interval] age - . 0876399 0276829 -3. 17 0. 002 - . 1419453 . 0333345 educ . 122375 . 1377259 0.89 0. 374 - . 1478015 . 3925516 _cons 43.4232 2. 236407 19.42 0.090 39. 03605 47 .81035