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ISYE 6414 FINAL EXAM / ISYE 6414 FINAL EXAM REAL EXAM QUESTION AND ANSWERS

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ISYE 6414 FINAL EXAM / ISYE 6414 FINAL EXAM REAL EXAM QUESTION AND ANSWERS 3. If considering only BIC for the model selection criterion, a model with lower BIC is preferred overa model with higher BIC. a. True b. False Sol: True. A lower BIC corresponds to a lower penalized prediction risk, and the statementspecified that is the only thing we are considering. 14. If we do not penalize the training risk, our variable selection method would always prefer morecomplex models. a. True b. False Sol: True. See Unit 5.1.3

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ISYE 6414 FINAL EXAM 2022-2023 / ISYE 6414 FINAL
EXAM REAL EXAM QUESTION AND ANSWERS


1. We should always use mean squared error to determine the best value
of lambda in lasso regression.
a. True
b. False

Sol: False. The criterion used is a choice we make.
2. Standard linear regression is an example of a generalized linear model
where the response is normally distributed and the link is the identity
function.
a. True
b. False

Sol: True. See Unit 4.4.1.
3. Goodness-of-fit assessment for logistic regression involves checking
for the independence, constant variance, and normality of the deviance
residuals.
a. True
b. False

Sol: False. We don’t have constant variance in binomial regression.
4. You are interested in understanding the relationship between stress level and
exercise, with stress as the response. In your model, the number of hours a
person spends exercising per week would be considered an explanatory
variable while the person’s age would be a controlling variable.
a. True
b. False

Sol: True. Time spent exercising is part of the relationship you are trying to
understand while age could act as a confounding variable that you need to
control.
5. The hypothesis test for goodness-of-fit using Pearson residuals and the
test using deviance residuals will always reach the same conclusion.
a. True
b. False

Sol: False. One test may conclude plausibly good fit while the other rejects it.

, 6. A logistic regression model with high goodness of fit can have low predictive
power.
a. True
b. False

Sol: True. See Unit 4.2.3.

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