ISYE 6414 CORRECT FINAL EXAM QUESTIONS AND
ANSWERS SURE A+
✔✔True - the deviance residuals are approximately N(0,1) if the model is a good fit to
the data. - ✔✔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 logit link function is the best link function to model binary response
data because it always fits the data better than other link functions.
✔✔True - we can use the Pearson or deviance residuals, but only if the model has
replications. - ✔✔Although there are no error terms in logistic regression model using
binary data with replications, we can still perform residual analysis.
✔✔True - The error rate is biased downwards, since the model sees the data 2 times,
once for training and once for testing. - ✔✔For a classification model, the training error
tends to underestimate the true classification error rate of the model.
✔✔True - the parameters and their standard errors are approximate. - ✔✔The
estimated regression coefficients in Poisson regression are approximate.
✔✔False - we use a z-test, since the the distributions are approximately normal with
large N. - ✔✔A t-test is used for testing the statistical significance of a coefficient given
all predicting variables in a Poisson regression model.
✔✔True - ✔✔An overdispersion parameter of 1 indicates that the variability of the
response is close to the variability estimated by the model.
✔✔False - we assume that the log rate is a linear combination of the predicting
variables, hence Poisson regression is a generalized linear model (GLM) - ✔✔In
, Poisson regression, we assume a non linear relationship between the log rate and the
predicting variables.
✔✔True - ✔✔Logistic regression models the probability of a success given a set of
predicting variables.
✔✔True - ✔✔The estimation of logistic regression coefficients is based on maximum
likelihood.
✔✔1) no error term
2) the response variable is not normally distributes (binomial)
3) it models probability, not expectation of response - ✔✔What are the differences
between logistic regression and standard regression.
✔✔False - Come back and name other link functions - ✔✔The logit link function is the
only link function that can be used for modeling binary response data.
✔✔False - logistic regression coefficients are interpreted with respect to odds - ✔✔The
interpretation of the regression coefficients is the same in logistic regression as
standard regression.
✔✔False - there is no closed form solution, we we use a numerical approximation. -
✔✔We can derive exact estimates for the logistic regression coefficients.
✔✔False - fill in later - ✔✔The estimations of the regression coefficients is based on
minimizing the sum of least squares in logistic regression.
✔✔1) The sampling distribution of the regression coefficients is approximate
2) a large sample size is required for making accurate statistical inferences
3) a normal sampling distribution is used instead of a t-distribution for statistical
inference - ✔✔Differences between logistic regression and linear regression - statistical
inference.
✔✔True - statistical inference in logistic regression is only reliable when N is large -
✔✔in logistic regression, the hypothesis test for subsets of coefficients is approximate, it
relies on a large sample size.
✔✔True - we predict if a response will be a success or failure - ✔✔In logistic regression,
prediction is a classification of a future binary response.
✔✔True - ✔✔in k-fold cross validation, the larger K, the higher the variability in the
estimation of the classification error is.
✔✔1) to model count data
ANSWERS SURE A+
✔✔True - the deviance residuals are approximately N(0,1) if the model is a good fit to
the data. - ✔✔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 logit link function is the best link function to model binary response
data because it always fits the data better than other link functions.
✔✔True - we can use the Pearson or deviance residuals, but only if the model has
replications. - ✔✔Although there are no error terms in logistic regression model using
binary data with replications, we can still perform residual analysis.
✔✔True - The error rate is biased downwards, since the model sees the data 2 times,
once for training and once for testing. - ✔✔For a classification model, the training error
tends to underestimate the true classification error rate of the model.
✔✔True - the parameters and their standard errors are approximate. - ✔✔The
estimated regression coefficients in Poisson regression are approximate.
✔✔False - we use a z-test, since the the distributions are approximately normal with
large N. - ✔✔A t-test is used for testing the statistical significance of a coefficient given
all predicting variables in a Poisson regression model.
✔✔True - ✔✔An overdispersion parameter of 1 indicates that the variability of the
response is close to the variability estimated by the model.
✔✔False - we assume that the log rate is a linear combination of the predicting
variables, hence Poisson regression is a generalized linear model (GLM) - ✔✔In
, Poisson regression, we assume a non linear relationship between the log rate and the
predicting variables.
✔✔True - ✔✔Logistic regression models the probability of a success given a set of
predicting variables.
✔✔True - ✔✔The estimation of logistic regression coefficients is based on maximum
likelihood.
✔✔1) no error term
2) the response variable is not normally distributes (binomial)
3) it models probability, not expectation of response - ✔✔What are the differences
between logistic regression and standard regression.
✔✔False - Come back and name other link functions - ✔✔The logit link function is the
only link function that can be used for modeling binary response data.
✔✔False - logistic regression coefficients are interpreted with respect to odds - ✔✔The
interpretation of the regression coefficients is the same in logistic regression as
standard regression.
✔✔False - there is no closed form solution, we we use a numerical approximation. -
✔✔We can derive exact estimates for the logistic regression coefficients.
✔✔False - fill in later - ✔✔The estimations of the regression coefficients is based on
minimizing the sum of least squares in logistic regression.
✔✔1) The sampling distribution of the regression coefficients is approximate
2) a large sample size is required for making accurate statistical inferences
3) a normal sampling distribution is used instead of a t-distribution for statistical
inference - ✔✔Differences between logistic regression and linear regression - statistical
inference.
✔✔True - statistical inference in logistic regression is only reliable when N is large -
✔✔in logistic regression, the hypothesis test for subsets of coefficients is approximate, it
relies on a large sample size.
✔✔True - we predict if a response will be a success or failure - ✔✔In logistic regression,
prediction is a classification of a future binary response.
✔✔True - ✔✔in k-fold cross validation, the larger K, the higher the variability in the
estimation of the classification error is.
✔✔1) to model count data
