ECN 410 practice exam 3
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1. Referred to output a and your knowledge in order to answer the question:
least squares estimates are still blue: 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
2. 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: True
straight from the slides (endogeneity lecture)
3. 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 endogene-
ity): 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.)
1/8
, ECN 410 practice exam 3
Study online at https://quizlet.com/_htkhca
4. 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): False
both income and density are correlated with the error term
5. We would always like to reject the null hypothesis when testing for normality
of the errors: False
6. 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 regres-
sion model: Only 1 is false
(The plot of the residuals versus X shows that the residual variance tends to increase with X)
7. When a researcher leaves important independent variables out of the regres-
sion equation, the estimates are not BLUE anymore: True
8. Using SAS output B which of the classical assumptions were tested
1) multicollinearity
2) normality of the errors
3) linearity
4) e=0: Only 1 is false
9. BLUE means best linear untransformed estimators: False
10. When testing for normality, SAS output give 3 goodness of fit tests. The
decision is based on when the majority reach agreement: True
11. When a researcher leaves important independent variable(s) out of the
regression equation he or she violates the following classical assumption:
observations of the error term are uncorrelated with each other: True
2/8
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1. Referred to output a and your knowledge in order to answer the question:
least squares estimates are still blue: 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
2. 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: True
straight from the slides (endogeneity lecture)
3. 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 endogene-
ity): 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.)
1/8
, ECN 410 practice exam 3
Study online at https://quizlet.com/_htkhca
4. 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): False
both income and density are correlated with the error term
5. We would always like to reject the null hypothesis when testing for normality
of the errors: False
6. 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 regres-
sion model: Only 1 is false
(The plot of the residuals versus X shows that the residual variance tends to increase with X)
7. When a researcher leaves important independent variables out of the regres-
sion equation, the estimates are not BLUE anymore: True
8. Using SAS output B which of the classical assumptions were tested
1) multicollinearity
2) normality of the errors
3) linearity
4) e=0: Only 1 is false
9. BLUE means best linear untransformed estimators: False
10. When testing for normality, SAS output give 3 goodness of fit tests. The
decision is based on when the majority reach agreement: True
11. When a researcher leaves important independent variable(s) out of the
regression equation he or she violates the following classical assumption:
observations of the error term are uncorrelated with each other: True
2/8
