ISYE 6414 Final Exam 2025 UPDATE Verified
Questions And Answers | Guaranteed Success!!
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 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.
Questions And Answers | Guaranteed Success!!
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 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.
