1. If you accidentally forget to use the robust standard errors option in your regression software, then: A) both your coefficients and standard errors will be different than in case with robust SE B) only your standard errors will be different than in case with robust SE C) only your coefficients will be different than in case with robust SE D) only the R squared will be different than in the case with robust SE 2. We saw that the OLS estimator from a regression of test scores on a dummy for class size (X=1 for STR<20) was positive and equal to 7.4. If average family income is negatively correlated with average class size in California school districts, we can expect the OLS estimator to be: A) larger than the true population value of the difference in means B) smaller than the true population value of the difference in means C) equal to the correlation between test scores and class size D) equal to the true population value of the difference in means
1. If you accidentally forget to use the robust standard errors option in your regression software, then:
A) both your coefficients and standard errors will be different than in case with robust SE
B) only your standard errors will be different than in case with robust SE
C) only your coefficients will be different than in case with robust SE
D) only the R squared will be different than in the case with robust SE
2. We saw that the OLS estimator from a regression of test scores on a dummy for class size (X=1 for STR<20) was positive and equal to 7.4. If average family income is
A) larger than the true population value of the difference in means
B) smaller than the true population value of the difference in means
C) equal to the
D) equal to the true population value of the difference in means
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