ISYE 6414 - UNIT 4 EXAM QUESTIONS
WITH CORRECT DETAILED ANSWERS
Which one is correct?
A) We can evaluate the goodness of fit a model using the testing procedure of the
overall regression.
B) In applying the deviance test for goodness of fit in logistic regression, we seek large
p-values, that is, not reject the null hypothesis.
C) There is no error term in logistic regression and thus we cannot perform a goodness
of fit assessment.
D) None of the above. - Answer-B
Which is correct?
A) Prediction translates into classification of a future binary response in logistic
regression.
B) In order to perform classification in logistic regression, we need to first define a
classifier for the classification error rate.
C) One common approach to estimate the classification error is cross-validation.
D) All of the above. - Answer-D
Comparing cross-validation methods,
A) The random sampling approach is more computational efficient that leave-one-out
cross validation.
B) In K-fold cross-validation, the larger K is, the higher the variability in the estimation of
the classification error is.
C) Leave-one-out cross validation is a particular case of the random sampling cross-
validation.
D) None of the above. - Answer-B
Generalized Linear Model - Answer-To generalize the standard regression model to
response data that do not have a normal distribution, this generalizes the linear model
to response data coming from other distributions.
In GLM or generalized linear models, the response Y is assumed to have what kind of
distribution? - Answer-distribution from the exponential family of distributions
Poisson regression - Answer-commonly used for modeling count or rate data.
What is the difference between using Poisson regression versus the standard
regression with, say, with the log transformation of the response variable? - Answer-In
standard regression, the variance is assumed constant. In Poisson, the variance of the
, response is assumed to be equal to the expectation, since for the Poisson distribution,
the variance is equal to the expectation. Thus the variance is not constant.
Rate parameter - Answer-the expectation of the response Yi, given the predicting
variables, which is modeled as the exponential of the linear combination of the
predicting variables since the link function between expectation and the predicting
variables is the log function
log rate - Answer-the log function of the expected value of the response
what is the coefficient interpretation of a GLM (poisson)? - Answer-log ratio of the rate
with an increase with one unit in the predicting variable.
We do not interpret beta with respect to the response variable for a Poisson model but
with.... - Answer-respect to the ratio of the rate.
we estimate the Poisson model parameters using... - Answer-MLE
Poisson regression can be used:
A) To model count data.
B) To model rate response data.
C) To model response data with a Poisson distribution.
D) All of the above. - Answer-D
Which one is correct?
A) The standard normal regression, the logistic regression and the Poisson regression
are all falling under the generalized linear model framework.
B) If we were to apply a standard normal regression to response data with a Poisson
distribution, the constant variance assumption would not hold.
C) The link function for the Poisson regression is the log function.
D) All of the above. - Answer-D
In Poisson regression,
A) We model the log of the expected response variable not the expected log response
variable.
B) We use the ordinary least squares to fit the model.
C) There is an error term.
D) None of the above. - Answer-A
Which one is correct?
A) The estimated regression coefficients and their standard deviations are approximate
not exact in Poisson regression.
B) We use the glm() R command to fit a Poisson linear
regression.
C) The interpretation of the estimated regression coefficients is in terms of the ratio of
the response rates.
WITH CORRECT DETAILED ANSWERS
Which one is correct?
A) We can evaluate the goodness of fit a model using the testing procedure of the
overall regression.
B) In applying the deviance test for goodness of fit in logistic regression, we seek large
p-values, that is, not reject the null hypothesis.
C) There is no error term in logistic regression and thus we cannot perform a goodness
of fit assessment.
D) None of the above. - Answer-B
Which is correct?
A) Prediction translates into classification of a future binary response in logistic
regression.
B) In order to perform classification in logistic regression, we need to first define a
classifier for the classification error rate.
C) One common approach to estimate the classification error is cross-validation.
D) All of the above. - Answer-D
Comparing cross-validation methods,
A) The random sampling approach is more computational efficient that leave-one-out
cross validation.
B) In K-fold cross-validation, the larger K is, the higher the variability in the estimation of
the classification error is.
C) Leave-one-out cross validation is a particular case of the random sampling cross-
validation.
D) None of the above. - Answer-B
Generalized Linear Model - Answer-To generalize the standard regression model to
response data that do not have a normal distribution, this generalizes the linear model
to response data coming from other distributions.
In GLM or generalized linear models, the response Y is assumed to have what kind of
distribution? - Answer-distribution from the exponential family of distributions
Poisson regression - Answer-commonly used for modeling count or rate data.
What is the difference between using Poisson regression versus the standard
regression with, say, with the log transformation of the response variable? - Answer-In
standard regression, the variance is assumed constant. In Poisson, the variance of the
, response is assumed to be equal to the expectation, since for the Poisson distribution,
the variance is equal to the expectation. Thus the variance is not constant.
Rate parameter - Answer-the expectation of the response Yi, given the predicting
variables, which is modeled as the exponential of the linear combination of the
predicting variables since the link function between expectation and the predicting
variables is the log function
log rate - Answer-the log function of the expected value of the response
what is the coefficient interpretation of a GLM (poisson)? - Answer-log ratio of the rate
with an increase with one unit in the predicting variable.
We do not interpret beta with respect to the response variable for a Poisson model but
with.... - Answer-respect to the ratio of the rate.
we estimate the Poisson model parameters using... - Answer-MLE
Poisson regression can be used:
A) To model count data.
B) To model rate response data.
C) To model response data with a Poisson distribution.
D) All of the above. - Answer-D
Which one is correct?
A) The standard normal regression, the logistic regression and the Poisson regression
are all falling under the generalized linear model framework.
B) If we were to apply a standard normal regression to response data with a Poisson
distribution, the constant variance assumption would not hold.
C) The link function for the Poisson regression is the log function.
D) All of the above. - Answer-D
In Poisson regression,
A) We model the log of the expected response variable not the expected log response
variable.
B) We use the ordinary least squares to fit the model.
C) There is an error term.
D) None of the above. - Answer-A
Which one is correct?
A) The estimated regression coefficients and their standard deviations are approximate
not exact in Poisson regression.
B) We use the glm() R command to fit a Poisson linear
regression.
C) The interpretation of the estimated regression coefficients is in terms of the ratio of
the response rates.
