5/2/2020
Questions and answers (ISYE6501x) Final Quiz.
Verified Learners | Final Quiz | ISYE6501x Courseware | edX
Verifi ed Learners
180 Minute Time Limit
Instructions
Work alone. Do not collaborate with or copy from
anyone else. You may use any of the following resources:
Two sheets (both sides) of handwritten (not photocopied or scanned)
notes Blank scratch paper and pen/pencil
If any question seems ambiguous, use the most reasonable interpretation (i.e. don't be like
Calvin):
https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+1T2020/courseware/1fded79c7c304e148942f8f027c8716b/83d8d14b794f4dbfb6b201f4f… 1/30
,5/2/2020
Good Luck!
This the beginning of the Final Quiz. Please make sure that you submit all your answers
before the time runs out. Once you submit an answer to a question, you cannot change it.
There is no overall Submit
button.
Information for Question 1
There are eight questions labeled "Question 1." Answer all eight questions. For each of the
following eight
questions, select the type of problem that the model is best suited for. For each question there
may be more than one correct answer; you need only choose one. Each type of problem might
be used zero, one, or more
https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+1T2020/courseware/1fded79c7c304e148942f8f027c8716b/83d8d14b794f4dbfb6b201f4f… 2/30
, 5/2/2020
than one time in the eight questions.
Question 1
0.0/0.5 points (graded)
Select the type of problem that stepwise regression is best suited for. There may be more than
one correct answer; you need only choose one.
Classi cation
Clustering
Experimental design
Prediction from feature data
Prediction from time-series data
Variable selection
Submit You have used 1 of 1 attempt
Question 1
0.0/0.5 points (graded)
Select the type of problem that k-nearest-neighbor is best suited for. There may be more than one
correct answer; you need only choose one.
Classi cation
Clustering
Experimental design
Prediction from feature data
Prediction from time-series data
https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+1T2020/courseware/1fded79c7c304e148942f8f027c8716b/83d8d14b794f4dbfb6b201f4f… 3/30
Questions and answers (ISYE6501x) Final Quiz.
Verified Learners | Final Quiz | ISYE6501x Courseware | edX
Verifi ed Learners
180 Minute Time Limit
Instructions
Work alone. Do not collaborate with or copy from
anyone else. You may use any of the following resources:
Two sheets (both sides) of handwritten (not photocopied or scanned)
notes Blank scratch paper and pen/pencil
If any question seems ambiguous, use the most reasonable interpretation (i.e. don't be like
Calvin):
https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+1T2020/courseware/1fded79c7c304e148942f8f027c8716b/83d8d14b794f4dbfb6b201f4f… 1/30
,5/2/2020
Good Luck!
This the beginning of the Final Quiz. Please make sure that you submit all your answers
before the time runs out. Once you submit an answer to a question, you cannot change it.
There is no overall Submit
button.
Information for Question 1
There are eight questions labeled "Question 1." Answer all eight questions. For each of the
following eight
questions, select the type of problem that the model is best suited for. For each question there
may be more than one correct answer; you need only choose one. Each type of problem might
be used zero, one, or more
https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+1T2020/courseware/1fded79c7c304e148942f8f027c8716b/83d8d14b794f4dbfb6b201f4f… 2/30
, 5/2/2020
than one time in the eight questions.
Question 1
0.0/0.5 points (graded)
Select the type of problem that stepwise regression is best suited for. There may be more than
one correct answer; you need only choose one.
Classi cation
Clustering
Experimental design
Prediction from feature data
Prediction from time-series data
Variable selection
Submit You have used 1 of 1 attempt
Question 1
0.0/0.5 points (graded)
Select the type of problem that k-nearest-neighbor is best suited for. There may be more than one
correct answer; you need only choose one.
Classi cation
Clustering
Experimental design
Prediction from feature data
Prediction from time-series data
https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+1T2020/courseware/1fded79c7c304e148942f8f027c8716b/83d8d14b794f4dbfb6b201f4f… 3/30
