ECN 410 practice exam 3 Questions and Answers Already Passed Latest Update 2025-2026
Referred to output a and your knowledge in order to answer the question: least squares
estimates are still blue - Answers False
density and income are not included in the model, they become correlated error, increasing SE
of the regression (variance) and the coefficients become biased
Consider the following fitted regression model:
square root miles = 8.24 + 0.23 income + 0.24 age - 1.13 kids
the correct interpretation is: an increase of $1000 in income, will increase on average, square
root of 230 miles or 15.16 miles, holding everything else constant - Answers True
straight from the slides (endogeneity lecture)
In the study of 28 industrial establishments of of varying size, the number of supervised workers
X and the number of supervisors y were recorded. A regression model was developed to
investigate the relationship between Y and X. use output C to answer the question: which
classical assumption was violated?
1) the regression model is linear is correctly specified and has a additive error term
2) the error term has zero population mean
3) the error term has a constant various (homogeneous variance)
4) the error term is normally distributed
5) all explanatory variables are uncorrelated with the error term (no endogeneity) - Answers 3)
the error term has a constant variance ( homogenous variance)
1) (No violation of CA. The Lack of Fit test showed a linear relationship.)
2) (No violation of CA. The SAS output shows the mean of residuals = 0)
3) (True. The hypothesis of constant variance was rejected.)
4) (No violation of CA. Normality tests showed p-values > 0.05.)
5) (True, even though there is no correlation output, the data contains only Y and X, therefore,
including X in the regression model eliminates any specification bias.)
, Referred to output A and your knowledge in order to answer the question: All the explanatory
variables are uncorrelated with the error term (use a = .10) - Answers False
both income and density are correlated with the error term
We would always like to reject the null hypothesis when testing for normality of the errors -
Answers False
Use output C to answer the question about the residuals:
1) the plot are the residuals versus X shows that the residual variance tends to decrease with X
2) the residuals tend to lie in a band that diverges as one of those along the x-axis
3) because the band within which the residuals lie diverges as X increases the error variance is
also increasing with x
4) a plot of the residuals against the predictor variable points up of the the presence of
heteroskedastity errors
5) evidence that we need to transform the dependent variable to fix the regression model -
Answers Only 1 is false
(The plot of the residuals versus X shows that the residual variance tends to increase with X)
When a researcher leaves important independent variables out of the regression equation, the
estimates are not BLUE anymore - Answers True
Using SAS output B which of the classical assumptions were tested
1) multicollinearity
2) normality of the errors
3) linearity
4) e=0 - Answers Only 1 is false
BLUE means best linear untransformed estimators - Answers False
When testing for normality, SAS output give 3 goodness of fit tests. The decision is based on
when the majority reach agreement - Answers True
When a researcher leaves important independent variable(s) out of the regression equation he
or she violates the following classical assumption:
Referred to output a and your knowledge in order to answer the question: least squares
estimates are still blue - Answers False
density and income are not included in the model, they become correlated error, increasing SE
of the regression (variance) and the coefficients become biased
Consider the following fitted regression model:
square root miles = 8.24 + 0.23 income + 0.24 age - 1.13 kids
the correct interpretation is: an increase of $1000 in income, will increase on average, square
root of 230 miles or 15.16 miles, holding everything else constant - Answers True
straight from the slides (endogeneity lecture)
In the study of 28 industrial establishments of of varying size, the number of supervised workers
X and the number of supervisors y were recorded. A regression model was developed to
investigate the relationship between Y and X. use output C to answer the question: which
classical assumption was violated?
1) the regression model is linear is correctly specified and has a additive error term
2) the error term has zero population mean
3) the error term has a constant various (homogeneous variance)
4) the error term is normally distributed
5) all explanatory variables are uncorrelated with the error term (no endogeneity) - Answers 3)
the error term has a constant variance ( homogenous variance)
1) (No violation of CA. The Lack of Fit test showed a linear relationship.)
2) (No violation of CA. The SAS output shows the mean of residuals = 0)
3) (True. The hypothesis of constant variance was rejected.)
4) (No violation of CA. Normality tests showed p-values > 0.05.)
5) (True, even though there is no correlation output, the data contains only Y and X, therefore,
including X in the regression model eliminates any specification bias.)
, Referred to output A and your knowledge in order to answer the question: All the explanatory
variables are uncorrelated with the error term (use a = .10) - Answers False
both income and density are correlated with the error term
We would always like to reject the null hypothesis when testing for normality of the errors -
Answers False
Use output C to answer the question about the residuals:
1) the plot are the residuals versus X shows that the residual variance tends to decrease with X
2) the residuals tend to lie in a band that diverges as one of those along the x-axis
3) because the band within which the residuals lie diverges as X increases the error variance is
also increasing with x
4) a plot of the residuals against the predictor variable points up of the the presence of
heteroskedastity errors
5) evidence that we need to transform the dependent variable to fix the regression model -
Answers Only 1 is false
(The plot of the residuals versus X shows that the residual variance tends to increase with X)
When a researcher leaves important independent variables out of the regression equation, the
estimates are not BLUE anymore - Answers True
Using SAS output B which of the classical assumptions were tested
1) multicollinearity
2) normality of the errors
3) linearity
4) e=0 - Answers Only 1 is false
BLUE means best linear untransformed estimators - Answers False
When testing for normality, SAS output give 3 goodness of fit tests. The decision is based on
when the majority reach agreement - Answers True
When a researcher leaves important independent variable(s) out of the regression equation he
or she violates the following classical assumption:
