ISYE 6414 FINAL EXAM 2026 UPDATE QUESTIONS
AND CORRECT VERIFIED ANSWERS ALREADY
GRADED A+ (BRAND NEW VISION)
In Poisson regression we model the error term - ANS-False - there is no error term
One problem with fitting a normal regression model to Poisson data is the departure from the
assumption of constant variance - ANS-True
Event rates can be calculated as events per units of varying size, this unit of size is called
exposure - ANS-True
The estimators for the regression coefficients in the Poisson regression are biased. - ANS-False -
they are unbiased
To perform hypothesis testing for Poisson, we can use again the approximate normal sampling
distribution, also called the Wald test - ANS-True - Wald Test also used with logistic regression
Hypothesis testing for Poisson regression can be done on small sample sizes - ANS-False -
Approximation of normal distribution needs large sample sizes, so does hypothesis testing.
For large sample size data, the distribution of the test statistic, assuming the null hypothesis, is a
chi-squared distribution - ANS-True
The p-value of the test computed as the left tail of the chi-squared distribution - ANS-False -
Right tail
, Poisson Assumptions - log transformation of the rate is a linear combination of the predicting
variables, the response variables are independently observed, the link function g is the log
function - ANS-True - remember, NO ERROR TERM
Overdispersion is when the variability of the response variable is larger than estimated by the
model - ANS-True
The gam() function is a non-parametric test to determine what transformation is best. - ANS-
True
The deviance and pearson residuals are normally distributed - ANS-TRUE - the residual
deviances are chi square distributed
Model with many predictors have high bias but low variance. - ANS-False - low bias and high
variance
When the objective is to explain the relationship to the response, one might consider including
predicting variables which are correlated - ANS-True - But this should be avoided for prediction
Variable selection addresses multicolinearity, high dimensionaltiy, and prediction vs explanatory
prediction - ANS-TRUE
The variables chosen for prediction and the variables chosen for explanatory objectives will be
the same. - ANS-False
Variable selection is not special, it is affected by highly correlated variables - ANS-TRUE
Confounding variable is a variable that influences both the dependent variable and independent
variable - ANS-True
AND CORRECT VERIFIED ANSWERS ALREADY
GRADED A+ (BRAND NEW VISION)
In Poisson regression we model the error term - ANS-False - there is no error term
One problem with fitting a normal regression model to Poisson data is the departure from the
assumption of constant variance - ANS-True
Event rates can be calculated as events per units of varying size, this unit of size is called
exposure - ANS-True
The estimators for the regression coefficients in the Poisson regression are biased. - ANS-False -
they are unbiased
To perform hypothesis testing for Poisson, we can use again the approximate normal sampling
distribution, also called the Wald test - ANS-True - Wald Test also used with logistic regression
Hypothesis testing for Poisson regression can be done on small sample sizes - ANS-False -
Approximation of normal distribution needs large sample sizes, so does hypothesis testing.
For large sample size data, the distribution of the test statistic, assuming the null hypothesis, is a
chi-squared distribution - ANS-True
The p-value of the test computed as the left tail of the chi-squared distribution - ANS-False -
Right tail
, Poisson Assumptions - log transformation of the rate is a linear combination of the predicting
variables, the response variables are independently observed, the link function g is the log
function - ANS-True - remember, NO ERROR TERM
Overdispersion is when the variability of the response variable is larger than estimated by the
model - ANS-True
The gam() function is a non-parametric test to determine what transformation is best. - ANS-
True
The deviance and pearson residuals are normally distributed - ANS-TRUE - the residual
deviances are chi square distributed
Model with many predictors have high bias but low variance. - ANS-False - low bias and high
variance
When the objective is to explain the relationship to the response, one might consider including
predicting variables which are correlated - ANS-True - But this should be avoided for prediction
Variable selection addresses multicolinearity, high dimensionaltiy, and prediction vs explanatory
prediction - ANS-TRUE
The variables chosen for prediction and the variables chosen for explanatory objectives will be
the same. - ANS-False
Variable selection is not special, it is affected by highly correlated variables - ANS-TRUE
Confounding variable is a variable that influences both the dependent variable and independent
variable - ANS-True
