OMSA Midterm 2 Exam Study Guide with Complete Solutions
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Course
2024/2025
Institution
2024/2025
OMSA Midterm 2 Exam Study Guide
with Complete Solutions
Overfitting - Answer-Number of factors is too close to or larger than number of data points -- fitting
to both real effects and random effects. Comes from including too many variables!
Ways to avoid overfitting - Answer-- Need number of fa...
means: at each step it does the one thing that looks best withouth 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.
continue till there's no factor bad enough to remove and model doesn't have any more factors we want.
step-wise regression - Answer✔✔-combination of forward and backward elimination -- beginning with
either all factors or no factors.
after adding each new factor (and at end) -- eliminate right away any factors that no longer appear any
good. future options are not considered.
could use AIC or BIC, or model's R squared to pick factor to add/remove in fwd, bckwd, or stepwise
lasso method - Answer✔✔-adds constraint tau to std reg model. chooses coefficients to min sum of
squared errors.
right value of tau dependent on 2 things:
1) # of variables you want
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