8/17/2021 Final Quiz | Final Quiz - Summer 2021 | SU21: Introduction to Analytics Modeling | edX
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SU21: Introduction to Analytics Modeling
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,8/17/2021 Final Quiz | Final Quiz - Summer 2021 | SU21: Introduction to Analytics Modeling | edX
Final Quiz due Aug 5, 2021 02:00 EDT Completed
210 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) note
Blank scratch paper and pen/pencil
If any question seems ambiguous, use the most reasonable interpretation (i.e
If you experience any technical issues (i.e. Math Processing Error), please
selected answers and refresh the page. If the issue persists, then please f
the Instructors know about the issue in a private Piazza post afterwards.
Good Luck!
This is the beginning of the Final Quiz. Please make sure that you submit all y
the time runs out. Once you submit an answer to a question, you cannot cha
overall Submit button.
After submitting all answers, please click the "End my Exam" button, above,
, rate is 2 calls/minute. Once an operator answers a call (at any time), it takes an
8/17/2021 Final Quiz | Final Quiz - Summer 2021 | SU21: Introduction to Analytics Modeling | edX
to complete the call.
[NOTE: This is a simplified version of the call center system. If you have deepe
call centers work, please do not use it for this question; you would end up mak
complex than it is designed to be.]
a. The first model the airline tries is a queuing model with 5 operators always a
you expect the queuing model to show?
Wait times are low at both busy and non-busy times.
Wait times are low at busy times and high at non-busy times.
Wait times are low at non-busy times and high at busy times.
Wait times are high at both busy and non-busy times.
b. The second model the airline tries is a queuing model with 100 operators ava
times and 25 operators available during non-busy times. What would you expe
to show?
Wait times are low at both busy and non-busy times.
Wait times are low at busy times and high at non-busy times.
Wait times are low at non-busy times and high at busy times.
Wait times are high at both busy and non-busy times.
The airline now has decided that, when there are 50 calls waiting, the airline w
operator that can handle 20 calls simultaneously. The AI operator then stays o
are waiting.
The airline would like to model this new process with a Markov chain, where ea
of calls waiting (e.g., 0 calls waiting, 1 call waiting, etc.).
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