ISYE 6414 FINAL EXAM 2026 UPDATE
QUESTIONS AND CORRECT VERIFIED ANSWERS
ALREADY GRADED A+ (BRAND NEW VISION)
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
Explanatory variable is one that explains changes in the response variable - ANS-TRUE
Predicting variable is used in regression to predict the outcome of another variable. - ANS-True
It is good practice apply variable selection without understanding the problem at hand to
reduce bias. - ANS-False - always understand the problem at hand to better select variables for
the model.
When a statistically insignificant variable is discarded from the model, there is little change in
the other predictors statistical significance. - ANS-False - it is possible that when a predictor is
discarded, the statistical significance of other variables will change.
QUESTIONS AND CORRECT VERIFIED ANSWERS
ALREADY GRADED A+ (BRAND NEW VISION)
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
Explanatory variable is one that explains changes in the response variable - ANS-TRUE
Predicting variable is used in regression to predict the outcome of another variable. - ANS-True
It is good practice apply variable selection without understanding the problem at hand to
reduce bias. - ANS-False - always understand the problem at hand to better select variables for
the model.
When a statistically insignificant variable is discarded from the model, there is little change in
the other predictors statistical significance. - ANS-False - it is possible that when a predictor is
discarded, the statistical significance of other variables will change.
