ISYE 6414 OFFICIAL MIDTERM 1 PRACTICE
EXAMINATION 2026 QUESTIONS WITH
ANSWERS GRADED A+
◍ True.
Answer: In Poisson regression, we interpret the coefficients in terms of the
ratio of the response rates.
◍ Under the null hypothesis of good fit for logistic regression, the test statistic
has a Chi-Square distribution with n- p- 1 degrees of freedom.
Answer: True - don't forget, we want large P values
◍ The decision in using ANOVA table for testing whether a model is
significant depends on the normal distribution of the response variable.
Answer: True
◍ The variance estimator in logistic regression has a closed form expression..
Answer: False - use statistical software to obtain the variance-co-variance
matrix
◍ True - this is true for explanatory purposes but NOT prediction..
Answer: When Selecting variables for explanatory purpose, one might
consider including predicting variables which are correlated if it would help
answer your research hypothesis.
◍ The estimated regression coefficients from Lasso are less efficient than
those provided by the ordinary least squares.
Answer: True
◍ Simpson's Paradox - the reversal of association when looking at marginal vs
conditional relationships.
Answer: True
,◍ False - We interpret logistic regression coefficients with respect to the odds
of success..
Answer: In Logistic Regression, the estimated value for a regression
coefficient B represents the estimated expected change in the response
variable associated with a one unit increase in the predicting variable,
holding all else fixed.
◍ False - Ridge regression is associated with the L2 penalty, which does not
perform variable selection..
Answer: Ridge Regression is a regularized regression approach that can be
used for variable selection.
◍ True - the logit function is also known as the log-odds function, which is the
ln(P/1-p)..
Answer: The logit function is the log of the ratio of the probability of
success to the probability of failure and is also known as the log-odds
function.
◍ True.
Answer: When selecting variables, it is important to first establish which
variables are used for controlling bias in the sample and which are
explanatory.
◍ True - the deviance residuals are approximately N(0,1) if the model is a
good fit to the data..
Answer: For both logistic regression and Poisson regression, the deviance
residuals should follow an approximate standard normal distribution if the
model is a good fit for the data.
◍ False - the estimates for the coefficients are approximate in Poisson
regression..
Answer: in Poisson regression, inference relies on the exact sampling
distribution of the regression coefficients.
◍ In logistic regression we have an additional error term to estimate..
Answer: False - there is not error term in logistic regression.
, ◍ False - fill in later.
Answer: We can use residual analysis in Poisson regression to evaluate
whether errors are uncorrelated.
◍ True.
Answer: We can obtain both the estimates and the standard deviations of the
estimates for the regression coefficients in logistic regression.
◍ False - for logistic regression, if the p-value of the deviance test for GOD is
large, then the model is a good fit..
Answer: In Logistic regression, if the p-value of the deviance test for GOF is
smaller than the significance level alpha, then is is plausible that the model
is a good fit.
◍ False - regularized regression requires the predictors to be scaled..
Answer: It is not requires to standardize or rescale the predicting variables
when performing regularized regression.
◍ For large sample size data, the distribution of the test statistic, assuming the
null hypothesis, is a chi-squared distribution.
Answer: True
◍ We can use the z value to determine if a coefficient is equal to zero in
logistic regression..
Answer: True - z value = (Beta-0)/(SE of Beta)
◍ True.
Answer: In Logistic regression, the sampling distribution of the residual is
approximately normal if the data is a good fit.
◍ When the data may not be normally distributed, AIC is more appropriate for
variable selection than adjusted R-squared.
Answer: True
◍ False - when Lambda = 0, the corresponding regression coefficients are
equal to OL
S. .
EXAMINATION 2026 QUESTIONS WITH
ANSWERS GRADED A+
◍ True.
Answer: In Poisson regression, we interpret the coefficients in terms of the
ratio of the response rates.
◍ Under the null hypothesis of good fit for logistic regression, the test statistic
has a Chi-Square distribution with n- p- 1 degrees of freedom.
Answer: True - don't forget, we want large P values
◍ The decision in using ANOVA table for testing whether a model is
significant depends on the normal distribution of the response variable.
Answer: True
◍ The variance estimator in logistic regression has a closed form expression..
Answer: False - use statistical software to obtain the variance-co-variance
matrix
◍ True - this is true for explanatory purposes but NOT prediction..
Answer: When Selecting variables for explanatory purpose, one might
consider including predicting variables which are correlated if it would help
answer your research hypothesis.
◍ The estimated regression coefficients from Lasso are less efficient than
those provided by the ordinary least squares.
Answer: True
◍ Simpson's Paradox - the reversal of association when looking at marginal vs
conditional relationships.
Answer: True
,◍ False - We interpret logistic regression coefficients with respect to the odds
of success..
Answer: In Logistic Regression, the estimated value for a regression
coefficient B represents the estimated expected change in the response
variable associated with a one unit increase in the predicting variable,
holding all else fixed.
◍ False - Ridge regression is associated with the L2 penalty, which does not
perform variable selection..
Answer: Ridge Regression is a regularized regression approach that can be
used for variable selection.
◍ True - the logit function is also known as the log-odds function, which is the
ln(P/1-p)..
Answer: The logit function is the log of the ratio of the probability of
success to the probability of failure and is also known as the log-odds
function.
◍ True.
Answer: When selecting variables, it is important to first establish which
variables are used for controlling bias in the sample and which are
explanatory.
◍ True - the deviance residuals are approximately N(0,1) if the model is a
good fit to the data..
Answer: For both logistic regression and Poisson regression, the deviance
residuals should follow an approximate standard normal distribution if the
model is a good fit for the data.
◍ False - the estimates for the coefficients are approximate in Poisson
regression..
Answer: in Poisson regression, inference relies on the exact sampling
distribution of the regression coefficients.
◍ In logistic regression we have an additional error term to estimate..
Answer: False - there is not error term in logistic regression.
, ◍ False - fill in later.
Answer: We can use residual analysis in Poisson regression to evaluate
whether errors are uncorrelated.
◍ True.
Answer: We can obtain both the estimates and the standard deviations of the
estimates for the regression coefficients in logistic regression.
◍ False - for logistic regression, if the p-value of the deviance test for GOD is
large, then the model is a good fit..
Answer: In Logistic regression, if the p-value of the deviance test for GOF is
smaller than the significance level alpha, then is is plausible that the model
is a good fit.
◍ False - regularized regression requires the predictors to be scaled..
Answer: It is not requires to standardize or rescale the predicting variables
when performing regularized regression.
◍ For large sample size data, the distribution of the test statistic, assuming the
null hypothesis, is a chi-squared distribution.
Answer: True
◍ We can use the z value to determine if a coefficient is equal to zero in
logistic regression..
Answer: True - z value = (Beta-0)/(SE of Beta)
◍ True.
Answer: In Logistic regression, the sampling distribution of the residual is
approximately normal if the data is a good fit.
◍ When the data may not be normally distributed, AIC is more appropriate for
variable selection than adjusted R-squared.
Answer: True
◍ False - when Lambda = 0, the corresponding regression coefficients are
equal to OL
S. .
