ISYE 6414 Final Exam Review| UPDATE|COMPREHENSIVE
QUESTIONS AND VERIFIED SOLUTIONS/CORRECT
ANSWERS|GET 100% ACCURATE!! 2026 -2027
In logistic regression we have an additional error term to estimate.
False - there is not error term in logistic regression.
The least square estimation for the standard regression model is
equivalent with Maximum Likelihood Estimation, under the assumption
of normality.
True
The variance estimator in logistic regression has a closed form expression.
False - use statistical software to obtain the variance-co-variance matrix
We can use the z value to determine if a coefficient is equal to zero in logistic
regression.
True - z value = (Beta-0)/(SE of Beta)
In testing for a subset of coefficients in logistic regression the null hypothesis is that
the coefficient is equal to zero
True
,Like standard linear regression we can use the F test to test for overall
regression in logistic regression.
False - It's 1-pchisq(null deviance-residual deviance, DFnull-DFresidual)
For logistic regression we can define residuals for evaluating model goodness of
fit for models with and without replication.
False - can only be with replication under the assumption that Yi is binary and n1 is
greater than 1
The deviance residuals are the signed square root of the log-likelihood evaluated
at the saturated model
True
From the binomial approximation with a normal distribution using the central limit
theorem, the Pearson residuals have an approximately standard chi-squared
distribution.
False - Normal distribution
Visual Analytics for logistic regression
Normal probability plot of residuals
Residuals vs predictors
Logit of success rate vs predictors
True
Normal probability plot of residuals -
Normality Residuals vs predictors -
Linearity/Independence Logit of success rate
vs predictors - Linearity
, Under the null hypothesis of good fit for logistic regression, the test statistic has a
Chi- Square distribution with n- p- 1 degrees of freedom
True - don't forget, we want large P values
For the testing procedure for subsets of coefficients, we compare the likelihood
of a reduced model versus a full model. This is a goodness of fit test
False - it provides inference of the predictive power of the model
Predictive power means that the predicting variables predict the data even if
one or more of the assumptions do not hold.
True
One reason why the logistic model may not fit is the relationship between logit of
the expected probability and predictors might be multiplicative, rather than
additive
True
QUESTIONS AND VERIFIED SOLUTIONS/CORRECT
ANSWERS|GET 100% ACCURATE!! 2026 -2027
In logistic regression we have an additional error term to estimate.
False - there is not error term in logistic regression.
The least square estimation for the standard regression model is
equivalent with Maximum Likelihood Estimation, under the assumption
of normality.
True
The variance estimator in logistic regression has a closed form expression.
False - use statistical software to obtain the variance-co-variance matrix
We can use the z value to determine if a coefficient is equal to zero in logistic
regression.
True - z value = (Beta-0)/(SE of Beta)
In testing for a subset of coefficients in logistic regression the null hypothesis is that
the coefficient is equal to zero
True
,Like standard linear regression we can use the F test to test for overall
regression in logistic regression.
False - It's 1-pchisq(null deviance-residual deviance, DFnull-DFresidual)
For logistic regression we can define residuals for evaluating model goodness of
fit for models with and without replication.
False - can only be with replication under the assumption that Yi is binary and n1 is
greater than 1
The deviance residuals are the signed square root of the log-likelihood evaluated
at the saturated model
True
From the binomial approximation with a normal distribution using the central limit
theorem, the Pearson residuals have an approximately standard chi-squared
distribution.
False - Normal distribution
Visual Analytics for logistic regression
Normal probability plot of residuals
Residuals vs predictors
Logit of success rate vs predictors
True
Normal probability plot of residuals -
Normality Residuals vs predictors -
Linearity/Independence Logit of success rate
vs predictors - Linearity
, Under the null hypothesis of good fit for logistic regression, the test statistic has a
Chi- Square distribution with n- p- 1 degrees of freedom
True - don't forget, we want large P values
For the testing procedure for subsets of coefficients, we compare the likelihood
of a reduced model versus a full model. This is a goodness of fit test
False - it provides inference of the predictive power of the model
Predictive power means that the predicting variables predict the data even if
one or more of the assumptions do not hold.
True
One reason why the logistic model may not fit is the relationship between logit of
the expected probability and predictors might be multiplicative, rather than
additive
True
