ISYE 6501 - MIDTERM 2 EXAM
ISYE 6501 - MIDTERM 2 EXAM NEWEST 2025/2026 COMPLETE
QUESTIONS AND CORRECT DETAILED ANSWERS (VERIFIED
ANSWERS) GEORGIA INSTITUTE OF TECHNOLOGY-MAIN CAMPUS
|BRAND NEW VERSION!!
What is elastic net?
A variable selection method that works by minimizing the squared error
and constraining the combination of absolute values of coefficients and
their squares
what is a key difference between stepwise regresson and lasso
regression
If the data is not scaled, the coefficients can have artificially different
orders of magnitude, which means they'll have unbalanced effects on
the lasso constraint.
Why doesn't Ridge Regression perform variable selection?
The coefficients values are squared so they go closer to zero or
regularizes them
1|Page
, ISYE 6501 - MIDTERM 2 EXAM
What are the pros and cons of Greedy Algorithms (Forward selection,
stepwise elimination, stepwise regression)
Good for initial analysis but often don't perform as well on other data
because they fit more to random effects than you'd like and appear to
have a better fit
What are the pros and cons of LASSO and elastic net
They are slower but help make models that make better predictions
Which two methods does elastic net look like it combines and what are
the downsides from it?
Ridge Regression and LASSO.
Advantages: variable selection from LASSO and Predictive benefits of
LASSO.
Disadvantages: Arbitrarily rules out some correlated variables like
LASSO (don't know which one that is left out should be);
Underestimates coefficients of very predictive variables like Ridge
Regresison
What are some downsides of surveys?
Even if you what appears to be a representative sample in simple ways,
maybe it isn't in more complex ways.
2|Page
, ISYE 6501 - MIDTERM 2 EXAM
If we're testing to see whether red cars sell for higher prices than blue
cars, we need to account for the type and age of the cars in our data
set. This is called:
Controlling
what is a blocking factor
a source of variability that is not of primary interest to the experimenter
what is an example of a blocking factor
The type of car, sports car or family car, is a blocking factor that it could
account for some of the difference between red cars and blue cars.
Because sports cars are more likely to be red; if we account for the
difference, we can reduce the variability in our estimates
Under what conditions should you run A/B tests
When you can collect data quickly. When the data is representative and
the amount of data is small compared to the whole population
Do you have to decide the sample size ahead of time for A/B tests
no, and we can run the hypothesis test anytime we want
when might overfitting occur
when the # of factors is close to or larger than the # of data points
causing the model to potentially fit too closely to random effects
3|Page
, ISYE 6501 - MIDTERM 2 EXAM
Why are simple models better than complex ones
less data is required; less chance of insignificant factors and easier to
interpret
what is forward selection
we select the best new factor and see if it's good enough (R^2, AIC, or
p-value) add it to our model and fit the model with the current set of
factors. Then at the end we remove factors that are lower than a certain
threshold
what is backward elimination
we start with all factors and find the worst on a supplied threshold (p =
0.15). If it is worse we remove it and start the process over. We do that
until we have the number of factors that we want and then we move
the factors lower than a second threshold (p = .05) and fit the model
with all set of factors
4|Page
ISYE 6501 - MIDTERM 2 EXAM NEWEST 2025/2026 COMPLETE
QUESTIONS AND CORRECT DETAILED ANSWERS (VERIFIED
ANSWERS) GEORGIA INSTITUTE OF TECHNOLOGY-MAIN CAMPUS
|BRAND NEW VERSION!!
What is elastic net?
A variable selection method that works by minimizing the squared error
and constraining the combination of absolute values of coefficients and
their squares
what is a key difference between stepwise regresson and lasso
regression
If the data is not scaled, the coefficients can have artificially different
orders of magnitude, which means they'll have unbalanced effects on
the lasso constraint.
Why doesn't Ridge Regression perform variable selection?
The coefficients values are squared so they go closer to zero or
regularizes them
1|Page
, ISYE 6501 - MIDTERM 2 EXAM
What are the pros and cons of Greedy Algorithms (Forward selection,
stepwise elimination, stepwise regression)
Good for initial analysis but often don't perform as well on other data
because they fit more to random effects than you'd like and appear to
have a better fit
What are the pros and cons of LASSO and elastic net
They are slower but help make models that make better predictions
Which two methods does elastic net look like it combines and what are
the downsides from it?
Ridge Regression and LASSO.
Advantages: variable selection from LASSO and Predictive benefits of
LASSO.
Disadvantages: Arbitrarily rules out some correlated variables like
LASSO (don't know which one that is left out should be);
Underestimates coefficients of very predictive variables like Ridge
Regresison
What are some downsides of surveys?
Even if you what appears to be a representative sample in simple ways,
maybe it isn't in more complex ways.
2|Page
, ISYE 6501 - MIDTERM 2 EXAM
If we're testing to see whether red cars sell for higher prices than blue
cars, we need to account for the type and age of the cars in our data
set. This is called:
Controlling
what is a blocking factor
a source of variability that is not of primary interest to the experimenter
what is an example of a blocking factor
The type of car, sports car or family car, is a blocking factor that it could
account for some of the difference between red cars and blue cars.
Because sports cars are more likely to be red; if we account for the
difference, we can reduce the variability in our estimates
Under what conditions should you run A/B tests
When you can collect data quickly. When the data is representative and
the amount of data is small compared to the whole population
Do you have to decide the sample size ahead of time for A/B tests
no, and we can run the hypothesis test anytime we want
when might overfitting occur
when the # of factors is close to or larger than the # of data points
causing the model to potentially fit too closely to random effects
3|Page
, ISYE 6501 - MIDTERM 2 EXAM
Why are simple models better than complex ones
less data is required; less chance of insignificant factors and easier to
interpret
what is forward selection
we select the best new factor and see if it's good enough (R^2, AIC, or
p-value) add it to our model and fit the model with the current set of
factors. Then at the end we remove factors that are lower than a certain
threshold
what is backward elimination
we start with all factors and find the worst on a supplied threshold (p =
0.15). If it is worse we remove it and start the process over. We do that
until we have the number of factors that we want and then we move
the factors lower than a second threshold (p = .05) and fit the model
with all set of factors
4|Page
