ISYE 6414 Final Exam Questions And
Answers Updated 2024/2025
True - The relationship that links the predictors is highly non-linear. - answer✔In Logistic
Regression, the relationship between the probability of success and the predicting
variables is non-linear.
False - In logistic regression, there are no error terms. - answer✔In Logistic Regression, the
error terms follow a normal distribution.
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.
False - As there is no error term in logistic regression, there is no additional parameter for
the variance of the error terms. - answer✔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 number of parameters that need to be estimated in a standard linear
regression model with an intercept and same predicting variables.
False - log-likelihood is a non-linear function, and a numerical algorithm is needed in order
to maximize it. - answer✔The log-likelihood function is a linear function with a closed form
solution.
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 - The coefficient estimator follows an approximate normal distribution. -
answer✔Under logistic regression, the sampling distribution used for a coefficient
estimator is a chi-square distribution when the sample size is large.
False - when testing a subset of coefficients, deviance follows a chi-square distribution
with q degrees of freedom, where q is the number of regression coefficients discarded
ALL RIGHTS RESERVED.
from the full model to get the reduced model. - answer✔When testing a subset of
coefficients, deviance follows a chi-square distribution with q degrees of freedom, where q
is the number of regression coefficients in the reduced model.
True - logistic regression is the generalization of the standard regression model that is used
when the response variable y is binary or binomial. - answer✔Logistic regression deals
with the case where the dependent variable is binary and the conditional distribution is
binomial.
False - The residuals can only be defined for logistic regression with replications. -
answer✔It is good practice to perform a goodness-of-fit test on logistic regression models
without replications.
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 - GOF is no guarantee for good prediction and vice-versa. - answer✔If a logistic
regression model provides accurate classification, then we can conclude that it is a good
fir for the data.
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 - answer✔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.
- answer✔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. - answer✔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. - answer✔The estimated
regression coefficients in Poisson regression are approximate.
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