ISYE 6414 FINAL EXAM WITH CORRECT SOLUTIONS / ISYE6414 FINAL REAL EXAM 2023 NEW UPDATE LATEST (RATED A+)
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Course
ISYE 6414
Institution
ISYE 6414
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...
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. If we apply a Poisson regression model using a small sample
size, the estimators of the regression coefficient may not follow an approximate Normal
distribution, affecting the reliability of the statistical inference on the coefficients.
c. True
d. False
Sol: True. See Unit 4.2.1.
7. You fit a regression model using three predictors. You notice the estimated coefficient for
predictor X1 is an order of magnitude larger than the estimated coefficient for predictor
X2. It incorrect to conclude that X1 has a
greater effect on the response than X2.
a. True
b. False
Sol: False. We do not know that the variables are on the same scale in order to directly
compare them. We can only conclude that a 1-unit change in X1 is associated with a greater
change in the response than a 1-unit change in X2 holding other variables constant.
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