Question 0 -- Practice with Drag & Drop
0 points possible (ungraded)
Keyboard Help
Some of the quiz questions are Drag-and-Drop. You'll need to drag one or more answers to a
location.
Some answers might not be use
...
Question 0 -- Practice with Drag & Drop
0 points possible (ungraded)
Keyboard Help
Some of the quiz questions are Drag-and-Drop. You'll need to drag one or more answers to a
location.
Some answers might not be used at all, and some answers will be used once. To get full
credit you might need to drag more than one answer to some locations, just one answer to
other locations, and some locations might not have any correct answers.
Please do this quick practice question. The question will give you feedback to make sure
you've done it correctly, but the real quiz questions will not.
x=1,y=7
x=2,y=3 x=1,y=411/22/2020 GT
Correctly placed 3 items.
Good work! You have completed this drag and drop problem. Note that: (1) There are two places you
could've put (x=2,y=3); either one would be correct. (2) One location (x+y=2) had nothing dragged to it.
Another location had two answers dragged to it. (3) One choice (x=1,y=7) was not dragged anywhere,
since it wasn't correct for anything.
You have used 5 of 10 attempts.
Reset
Submit
Show Answer
Question 1
11/13 points (graded)
Keyboard Help
Drag each of the 13 models/methods to one of the 5 categories of question it is commonly
used for, unless no correct category is listed for it. For models/methods that have more than
one correct category, choose any one correct category; for models/methods that have no
correct category listed, do not drag them.
x=1,y=6
CUSUM Principal component
Correctly placed 9 items.
Misplaced 1 item.
Did not place 1 required item.
Good work! You have completed this drag and drop problem.
Submit You have used 1 of 1 attempts.
Reset
Show Answer
Support vector machine
k-means
ARIMA CART Exponential smoothing
k-nearest-neighbor Linear regression ogistic regression
Random forest
Cross validation
Question 2
2.46/3.0 points (graded)
Select all of the following models that are designed for use with attribute/feature data (i.e.,
not time-series data):
You have used 1 of 1 attempt
Final attempt was used, highest score is 11.0
ARIMA
CUSUM
Support vector machine
Random forest
k-nearest-neighbor
GARCH
k-means
Logistic regression
Exponential smoothing
Principal component analysis
Linear regression
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