ISYE 6501 Midterm 1 _ Intro Analytics Modeling - ISYE-6501-OAN_O01_QCH_ASY Georgia Institute Of Technology ISYE 6501
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Course
ISYE 6501 Introduction to Analytics
Institution
ISYE 6501 Introduction To Analytics
ISYE 6501 Midterm 1 : Intro Analytics Modeling - ISYE-6501-OAN/O01/QCH/ASY
ISYE 6501 Midterm 1
Due Mar 7 at 2am Points 100 Questions 45
Available Feb 24 at 2am - Mar 7 at 2am 11 days Time Limit 95 Minutes
Instructions
onorlock Chrome Extension
s exam requires Google Chrome and the Honorlock ...
ISYE 6501 Midterm 1
Due Mar 7 at 2am Points 100 Questions 45
Available Feb 24 at 2am - Mar 7 at 2am 11 days Time Limit 95 Minutes
Instructions
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Attempt History
Attempt Time Score
LATEST Attempt 1 58 minutes 90.25 out of 100
Score for this quiz: 90.25 out of 100
Submitted Mar 7 at 12:33am
This attempt took 58 minutes.
95 Minute Time Limit
Instructions
Work alone. Do not collaborate with or copy from anyone else.
You may use any of the following resources:
One sheet (both sides) of handwritten (not photocopied, scanned,
or printed) notes
If any question seems ambiguous, use the most reasonable
interpretation (i.e., don't be like Calvin):
If you experience any technical issues (i.e. images not loading)
you may refresh the page without interrupting your exam attempt.
If the issue persists, then please finish the exam and let the
Instructors know about the issue in a private Piazza post
afterwards.
Good luck!
Question 1 pts
1. For each of the 13 models/methods, select the choice that includes the
category of question it is commonly used for. For models/methods that
have more than one correct category, the one it is most commonly used
for; for models/methods that have no correct category listed, select
"None".
i. ARIMA Response prediction
ii. CART Classification and Response prediction
iii. Cross validation None of the other choices
iv. CUSUM None of the other choices
v. Exponential smoothing Response prediction
vi. GARCH Variance estimation
vii. kmeans Clustering
viii. k-nearest-neighbor Classification and Response prediction
ix. Linear regression Response prediction
x. Logistic regression Classification and Response prediction
xi. Principal component analysis None of the other choices
xii. Random forest Classification and Response prediction
xiii. Support vector machine Classification
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