ISYE 6414 REGRESSION ANALYSIS MIDTERM EXAM 2 COMPLETE ACTUAL Questions and Verified Solutions Latest Update This Year

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ISYE 6414 REGRESSION ANALYSIS MIDTERM EXAM 2
COMPLETE ACTUAL Questions and Verified Solutions
Latest Update This Year
QUESTION: 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




QUESTION: 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




QUESTION: If a variable is correlated but does not have multicolinearity, is this a problem? -
ANSWER-Not necessarily bruh




QUESTION: 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.

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QUESTION: True/False: The response variable in logistic regression is a binary response? -
ANSWER-True




QUESTION: True/False: In logistic regression, we model the probability of a success given the
predicting variables, not the response itself. - ANSWER-True




QUESTION: What are the assumptions for logistic regression? - ANSWER-Linearity Assumption

Independence Assumption

The G-Link function is a logit function Assumption

What is cooks distance used for? - ANSWER-It measures how much all of the values in the
regression model change with the ith observation is removed. Basically its a test for outliers




Rule of thumb: D denotes cooks distance, if D is > 4/n

OR D > 1 or any large D then it may be an outlier and should be removed.




QUESTION: If the normality assumption does not hold, we can pursue a transformation in the
response variable. T/F - ANSWER-True




QUESTION: 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.

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QUESTION: R^2 will always increase if we add more predicting variables. T/F - ANSWER-True




QUESTION: 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.




QUESTION: A statistic that effectively summarizes how well the X's are linearly related to Y is
the correlation coefficient. T/F - ANSWER-True




QUESTION: 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




QUESTION: 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.




QUESTION: How many regression coefficients are there for logistic regression? - ANSWER-Since
there is no error time, you have P + 1 with intercept.




QUESTION: Logistic regression is different from standard linear regression in that

a) It does not have an error term