BUSN 5000 BOARD EXAMINATION SET
2026 CORRECT ANSWERS GRADED A+
⩥ if we say E(y|x)=β1+β1x�(�|�)=�1+�1�, where β0�0 and β1�1
solve the population least-squares problem, then the CEF is the
population regression _____ and β0�0 and β1�1 are population
regression _____
Answer: function, coefficients
⩥ The population regression function provides the best ______ to the
CEF.
Answer: linear approximation
⩥ The population regression function provides the best _____ of the
dependent variable, given the explanatory variables
Answer: linear predictor
⩥ based on Yi = B0 + B1Xi! + ui, i = 1, ... N;
the coefficient B1 measures the _____ in y _____ with a _____ in x1, ,
holding all of the unobservables constant.
Answer: change, associated, unit change
,⩥ based on Yi = B0 + B1Xi! + ui, i = 1, ... N; if B0 and B1 solve the
population least-squares problem their values ______ the expected value
of the _____ difference between the dependent variable and the CEF.
Answer: minimize, squared
⩥ based on Yi = B0 + B1Xi! + ui, i = 1, ... N; the value of B1 that solves
the population least-squares problem is:
Answer: Cov(Xi1Yi)/E(xi1^2)
⩥ based on Yi = B0 + B1Xi! + ui, i = 1, ... N; the OLS estimator for B1
can be obtained by plugging in the _____ of Xi and Yi for their
_________ and plugging in another _______ for each outer expectation
Answer: Sample average, population average, sample average
⩥ based on Yi = B0 + B1Xi! + ui, i = 1, ... N; if there were more than
one X in the equation above, then the formula for B1 would be the
_______, except Xi1 would be replaced with the _________ from a
regression of Xi1 on the other Xs
Answer: same, residuals
⩥ The ______ theorem says you can control for other explanatory
variables in estimating the effect of an X on Y by either including the
other variables directly or regressing Y on the _________ from a
regression of X on the other variables
Answer: FWL, residuals
, ⩥ based on Yi = B0 + B1Xi! + ui, i = 1, ... N; when the PRF includes
more than one X, we say that B1 measures the _______ effect of X1
(without necessary giving a causal interpretation)
Answer: partial
⩥ based on Yi = B0 + B1Xi! + ui, i = 1, ... N; If E(Ui | Xi1) = 0 in (1),
Xi1 is ________ of Ui and the sampling error of B^1 equals _____ on
average, which implies that B^1 is ________.
Answer: mean independent, 0, unbiased
⩥ based on Yi = B0 + B1Xi! + ui, i = 1, ... N; If E(Ui | Xi1) = 0, the
sampling error of B^1 converges to _______ and B^1 is _______
Answer: 0, consistent
⩥ given Yi = B0 + B1Xi1 + B2Xi2c + Ui,, i = 1,.....N; if you omit Xi2
from the equation, B^1 will be unbiased only if B2 = _________ or Xi1
and Xi2 are __________.
Answer: 0, uncorrelated
⩥ given Yi = B0 + B1Xi1 + B2Xi2c + Ui,, i = 1,.....N; If you omit Xi2
from the equation, B^1 will be biased __________ if B2 and Cov(Xi1,
Xi2) have the same _________.
Answer: upward, sign
2026 CORRECT ANSWERS GRADED A+
⩥ if we say E(y|x)=β1+β1x�(�|�)=�1+�1�, where β0�0 and β1�1
solve the population least-squares problem, then the CEF is the
population regression _____ and β0�0 and β1�1 are population
regression _____
Answer: function, coefficients
⩥ The population regression function provides the best ______ to the
CEF.
Answer: linear approximation
⩥ The population regression function provides the best _____ of the
dependent variable, given the explanatory variables
Answer: linear predictor
⩥ based on Yi = B0 + B1Xi! + ui, i = 1, ... N;
the coefficient B1 measures the _____ in y _____ with a _____ in x1, ,
holding all of the unobservables constant.
Answer: change, associated, unit change
,⩥ based on Yi = B0 + B1Xi! + ui, i = 1, ... N; if B0 and B1 solve the
population least-squares problem their values ______ the expected value
of the _____ difference between the dependent variable and the CEF.
Answer: minimize, squared
⩥ based on Yi = B0 + B1Xi! + ui, i = 1, ... N; the value of B1 that solves
the population least-squares problem is:
Answer: Cov(Xi1Yi)/E(xi1^2)
⩥ based on Yi = B0 + B1Xi! + ui, i = 1, ... N; the OLS estimator for B1
can be obtained by plugging in the _____ of Xi and Yi for their
_________ and plugging in another _______ for each outer expectation
Answer: Sample average, population average, sample average
⩥ based on Yi = B0 + B1Xi! + ui, i = 1, ... N; if there were more than
one X in the equation above, then the formula for B1 would be the
_______, except Xi1 would be replaced with the _________ from a
regression of Xi1 on the other Xs
Answer: same, residuals
⩥ The ______ theorem says you can control for other explanatory
variables in estimating the effect of an X on Y by either including the
other variables directly or regressing Y on the _________ from a
regression of X on the other variables
Answer: FWL, residuals
, ⩥ based on Yi = B0 + B1Xi! + ui, i = 1, ... N; when the PRF includes
more than one X, we say that B1 measures the _______ effect of X1
(without necessary giving a causal interpretation)
Answer: partial
⩥ based on Yi = B0 + B1Xi! + ui, i = 1, ... N; If E(Ui | Xi1) = 0 in (1),
Xi1 is ________ of Ui and the sampling error of B^1 equals _____ on
average, which implies that B^1 is ________.
Answer: mean independent, 0, unbiased
⩥ based on Yi = B0 + B1Xi! + ui, i = 1, ... N; If E(Ui | Xi1) = 0, the
sampling error of B^1 converges to _______ and B^1 is _______
Answer: 0, consistent
⩥ given Yi = B0 + B1Xi1 + B2Xi2c + Ui,, i = 1,.....N; if you omit Xi2
from the equation, B^1 will be unbiased only if B2 = _________ or Xi1
and Xi2 are __________.
Answer: 0, uncorrelated
⩥ given Yi = B0 + B1Xi1 + B2Xi2c + Ui,, i = 1,.....N; If you omit Xi2
from the equation, B^1 will be biased __________ if B2 and Cov(Xi1,
Xi2) have the same _________.
Answer: upward, sign
