ISYE6414 FINAL COMPREHENSIVE
CERTIFICATION TEST PAPER QUESTIONS
AND SOLUTIONS GRADED A PLUS
VERIFIED
●● 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 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.
CERTIFICATION TEST PAPER QUESTIONS
AND SOLUTIONS GRADED A PLUS
VERIFIED
●● 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 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.
