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ISYE6414 FINAL EXAM BUNDLE
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ISYE6414 FINAL EXAM BUNDLE
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ISYE6414 FINAL EXAM BUNDLELogistic 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.
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
True or False? We use a chi-square testing procedure to test whether a subset of
regression coefficients are zero in Poisson regression. - ANSWER: True
Residual analysis in Poisson regression can be used: - ANSWER: To evaluate
goodness of fit of the model
When we do not have a good fit in generalized linear models, it may be that: -
ANSWER: We need to transform some of the predicting variables or to include
other variables; The variability of the expected rate is higher than estimated; There
may be leverage points that need to be explored further.
True or False: In logistic regression, the relationship between the probability of
success and the predicting variables is nonlinear. - ANSWER: True
True or False: In logistic regression, the error terms are assumed to follow a normal
distribution. - ANSWER: False. There are no error terms in logistic regression.
True or False: The logit function is the log of the ratio of the probability of success to
the probability of failure. It is also known as the log odds function. - ANSWER: True
True or False: The number of parameters that need to be estimated in a logistic
regression model with 6 predicting variables and an intercept is the same as the
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