ISYE6414 FINAL ACTUAL TEST QUESTIONS
AND CORRECT ANSWERS BUNDLED STUDY
MATERIAL COMPLETE GUIDE
●● If the normality assumption does not hold, we can pursue a
transformation in the response variable. T/F
Answer: True
●● If the linearity assumption does not hold, we can pursue a
transformation in the response variable. T/F
Answer: False, we pursue a transformation in the predictor variables.
●● R^2 will always increase if we add more predicting variables. T/F
Answer: True
●● If we want to compare models with different numbers of predicting
variables, what statistic should we use?
Answer: Adjusted R^2 because it adjusts for the number of predicting
variables. It doesn't increase when we add more predicting variables.
●● A statistic that effectively summarizes how well the X's are linearly
related to Y is the correlation coefficient. T/F
Answer: True
,●● T/F - The correlation coefficient cannot be used to evaluate the
correlation between the predicting variables for detecting (near) linear
dependence among the variables (or multicolinearity)
Answer: False, it CAN
●● How do you diagnose multicolinearity?
Answer: Calculate the VIF (variance inflation factor) for each predicting
variable
VIF = 1 / (1 - R^2j)
If VIF < max(10, 1 / (1- R^2)) then we got a problem
●● If a variable is correlated but does not have multicolinearity, is this a
problem?
Answer: Not necessarily bruh
●● What does the VIF measure
Answer: the VIF measures the proportional increase in the variance of
beta hat compared to what it would have been if the predicting variables
had been completely uncorrelated.
, ●● True/False: The response variable in logistic regression is a binary
response?
Answer: True
●● True/False: In logistic regression, we model the probability of a
success given the predicting variables, not the response itself.
Answer: True
●● What are the assumptions for logistic regression?
Answer: Linearity Assumption
Independence Assumption
The G-Link function is a logit function Assumption
●● What is the logit function?
Answer: ratio between the probability of success over probability of a
failure. So basically ratio between log of P over 1-p
●● What is the interpretation of the logistic regression coefficient?
Answer: The log of the odds ratio for an increase of one unit in the
predicting variable. We do not interpret beta with respect to the response
variable but with respect to the odds of success.
●● How many regression coefficients are there for logistic regression?
AND CORRECT ANSWERS BUNDLED STUDY
MATERIAL COMPLETE GUIDE
●● If the normality assumption does not hold, we can pursue a
transformation in the response variable. T/F
Answer: True
●● If the linearity assumption does not hold, we can pursue a
transformation in the response variable. T/F
Answer: False, we pursue a transformation in the predictor variables.
●● R^2 will always increase if we add more predicting variables. T/F
Answer: True
●● If we want to compare models with different numbers of predicting
variables, what statistic should we use?
Answer: Adjusted R^2 because it adjusts for the number of predicting
variables. It doesn't increase when we add more predicting variables.
●● A statistic that effectively summarizes how well the X's are linearly
related to Y is the correlation coefficient. T/F
Answer: True
,●● T/F - The correlation coefficient cannot be used to evaluate the
correlation between the predicting variables for detecting (near) linear
dependence among the variables (or multicolinearity)
Answer: False, it CAN
●● How do you diagnose multicolinearity?
Answer: Calculate the VIF (variance inflation factor) for each predicting
variable
VIF = 1 / (1 - R^2j)
If VIF < max(10, 1 / (1- R^2)) then we got a problem
●● If a variable is correlated but does not have multicolinearity, is this a
problem?
Answer: Not necessarily bruh
●● What does the VIF measure
Answer: the VIF measures the proportional increase in the variance of
beta hat compared to what it would have been if the predicting variables
had been completely uncorrelated.
, ●● True/False: The response variable in logistic regression is a binary
response?
Answer: True
●● True/False: In logistic regression, we model the probability of a
success given the predicting variables, not the response itself.
Answer: True
●● What are the assumptions for logistic regression?
Answer: Linearity Assumption
Independence Assumption
The G-Link function is a logit function Assumption
●● What is the logit function?
Answer: ratio between the probability of success over probability of a
failure. So basically ratio between log of P over 1-p
●● What is the interpretation of the logistic regression coefficient?
Answer: The log of the odds ratio for an increase of one unit in the
predicting variable. We do not interpret beta with respect to the response
variable but with respect to the odds of success.
●● How many regression coefficients are there for logistic regression?
