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OMSA Midterm 2 Exam Questions And Accurate Answers
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OMSA Midterm 2 Exam Questions And AccurateAnswers 2024-2025
Overfitting - Number of factors too close to, or larger than number of data points -- fitting
to both real effects and random effects. Includes too many variables in solution!
Avoiding overfitting - Need number of factors same order of magnitude as the number of
points
Need enough factors to get good fit from real effects and random effects
Simplicity - Answer Simple models are better than complex. When fewer factors exist,
less data collection is required -- less chance for including factors that are not
significant. Another reason for variable selection!
DOE - Answer systematic method to determine the relationship between factors
affecting a process and the output of that process.
must make sure either:
1) 2 data sets have same mix
2) break down data into smaller tests that test all factors, not just one.
Forward selection - Solution go step by step either narrowing or building a model --
begin with no factors.
Only add new factors with p-value ≤ 0.1 removing any factors above 0.05.
Greedy models - Solution forward selection, backward selection, stepwise regression
means: at each step it does the one thing that looks best without taking future options
into consideration
scaled variable selection models - Answer lasso, elastic net, (and ridge even though not
variable selection)
, backward selection - Answer start with model includes all factors and at each step find
worst factor and remove it from the model.
stop when there's no factor bad enough to remove and model no longer contains factors
we'd like to keep.
step-wise regression - Response combination of forward and backward elimination:
start with either all factors, or no factors
after each addition of new factor (and at end): immediately remove any factors that no
longer appear any good. Do not consider future options.
can use AIC or BIC, or model's R squared to decide which factor to add/remove in fwd,
bckwd, or stepwise
Lasso method - Answer adds penalty τ to std reg model. selects coefficients that
minimize the sum of squared errors.
right value of τ depends on two things :
1) how many variables you want
2) quality of model for allowing more variables
Correlation-- chooses only one to have nonzero coefficient. The other is left out.
SAG 19
elastic net-- Solution constrain a combination of absolute value and coefficients and
their squares--a combination of ridge and lasso.
must scale and choose appropriate value of τ+λ
ridge regression--Solution similar to elastic net but without absolute value term (lasso
part).
adds quadratic term that shrinks coefficient values- it pushes the reg coeff to 0 or
regularizes them. Adds bias but shrinks variance as cannot shrink to 0 as easily as
quadratic term
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