ISYE 6414 Final Exam Questions And
Answers Updated 2024/2025
Logistic regression is different from standard linear regression in that: - answer✔It does
not have an error term; The response variable is not normally distributed; It models
probability of a response and not the expectation of the response
Logistic regression models - answer✔The probability of a success given a set of predicting
variables
In logistic regression - answer✔The estimation of the regression coefficients is based on
maximum likelihood estimation
Using the R statistical software to fit a logistic regression, - answer✔We can obtain both
the estimates and the standard deviations of the estimates for the regression coefficients
Logistic regression is different from standard linear regression in that - answer✔The
sampling distribution of the regression coefficient is approximate; A large sample data is
required for making accurate statistical inferences; A normal sampling distribution is used
instead of a t-distribution for statistical inference.
In logistic regression, - answer✔The hypothesis test for subsets of coefficients is
approximate, it relies on a large sample size and is Chi-square
In logistic regression: - answer✔The sampling distribution of the residual is approximately
normal distribution if the model is a good fit.
True or False? In applying the deviance test for goodness of fit in logistic regression, we
seek large p-values, that is, not reject the null hypothesis. - answer✔True
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.
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C) One common approach to evaluate the classification error is cross-validation.
D) All of the above - answer✔D) All of the above
Comparing cross-validation methods, - answer✔In K-fold cross-validation, the larger K is,
the higher the variability in the estimation of the classification error is.
Poisson regression can be used: - answer✔To model count data.
To model rate response data.
To model response data with a Poisson distribution.
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) All of the above
In Poisson regression: - answer✔We model the log of the expected response variable not
the expected log response variable.
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.
D) All of the above - answer✔D) All of the above
In Poission regression - answer✔We make inference using z-intervals for the regression
coefficients; Statistical inference relies on approximate sampling; Statistical inference is
not reliable for small sample data
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