could this equation suffer from omitted variable bias explain could this equatio
ID: 3354547 • Letter: C
Question
could this equation suffer from omitted variable bias explain
could this equation suffer from omitted variable bias explain
800 1000 1200 1400 SAT Score regress gpa sat Source df MS 60 32.63 s 0.0000 0.3601 Adj R-squared = 0.3490 58019 Number of obs Model 10.9853267 Residual 19.5239667 F(1, 58) 1 10.9853267 Prob F 58 336620115 R-squared Total 30.5092933 59 .517106667 Root MSE p>It! [95% Conf. Interval] .0015665 0032564 0.680-,7270328 1.106756 gpa Coef. Std. Err. t sat cons 0024115 0004221 5.71 8.000 .1898614 .4580537 0.41
Explanation / Answer
Comparing the first and second equations/models, we can easily see that the first equation does suffer from omitted variables bias. The second equation shows that p-value for omitted variables in 1st equation that are now included are << 0.05, meaning that these variables have significant effect on the response variable (y). This can also be verified by the higher value of R-squared in the second equation/model which means that greater variance is being now explained by this equation/model.
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