G350 Exam 2 Review - Past Quizzes || with Accurate Answers 100%.
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When there are omitted variables in the regression, which are the determinants of the dependent variable, then: correct answers the OLS estimator is biased if the omitted variable is correlated with the included variable
If you have a regression model with two x's (independent variables), then o...
when there are omitted variables in the regression
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G350 Exam 2 Review - Past Quizzes || with Accurate
Answers 100%.
When there are omitted variables in the regression, which are the determinants of the dependent
variable, then: correct answers the OLS estimator is biased if the omitted variable is correlated
with the included variable
If you have a regression model with two x's (independent variables), then omitting one variable
which is relevant: correct answers can result in a negative value for the coefficient of the
included variable, even though the coefficient will have a significant positive effect on Y if the
omitted variable were included
In the multiple regression model, the least squares estimator is derived by: correct answers
minimizing the sum of squared predicted mistakes
In multiple regression, the R2 increases whenever a regressor is: correct answers added unless
the coefficient on the added regressor is exactly zero
Consider a multiple regression model with two independent variables, X1 and X2, where both
variables are determinants of the dependent variable. When omitting X2 from the regression,
then there will be omitted variable bias for B^: correct answers if X1 and X2 are correlated
Consider the multiple regression model with X1 and X2, where both variables are determinants
of the dependent variable. You first regress Y on only X1 and find no relationship. However,
when regressing Y on both X1 and X2, the slope coefficient, B^, changes by a large amount.
This suggests that your first regression suffers from: correct answers omitted variable bias
When used for causal inference, the multiple regression model permits estimating the effect of
Yi of changing one variable, X1: correct answers while holding all other X's in the model
constant
The reason for including many X's (control variables) in multiple regressions is to: correct
answers make the variables of interest no longer correlated with the error term, once the control
variables are held constant
If the estimates of the coefficients of interest change substantially across different models, such a
s moving from single regression to multiple regression: correct answers then this often provides
evidence that the original specification had omitted variable bias
Using multiple regression allows the interpretation of the estimated coefficients to say that a one
unit change in X results in a change in Y assuming, correct answers all other X's affecting Y are
held constant (ceteris paribus)
In the multiple regression model, the adjusted R2: correct answers will never be greater than the
regression R2
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