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ISYE-6501/ISYE 6501 Midterm 2 - Intro Analytics Modeling
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ISYE-6501/ISYE 6501 Midterm 2 - Intro Analytics Modeling
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ISYE 6501 Midterm 2 - Intro Analytics Modelingement is a more accessible alternative to drag & drop reordering. Pres
INSTRUCTIONS FOR QUESTIONS 1-5
For each of the following five questions, select the probability distribution that could best be
used to model the described scenario. Each distribution might be used, zero, one, or more
than one time in the five questions.
These scenarios are meant to be simple and straightforward; if you're an expert in the field
the question asks about, please do not rely on your expertise to fill in all the extra complexity
(you'll end up making the questions below more difficult than I intended).
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to move this question.
Question 1
1..4 pts
Number of people clicking an online banner ad each hour
Binomial
Exponential
Geometric
Correct!
Poisson
Weibull
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to move this question.
Question 2
1..4 pts
Time from when a generator is turned on until it fails
Binomial
,Exponential
Geometric
Poisson
Correct!
Weibull
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to move this question.
Question 3
1..4 pts
Number of hits to a real estate web site each minute
Binomial
Exponential
Geometric
Correct!
Poisson
Weibull
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to move this question.
Question 4
1..4 pts
Number of people entering a grocery store each minute
Binomial
Exponential
Geometric
Correct!
Poisson
Weibull
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to move this question.
Question 5
1..4 pts
Time between hits on a real estate web site
Binomial
Correct!
Exponential
Geometric
, Poisson
Weibull
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to move this question.
INFORMATION FOR QUESTIONS 6-7
Five classification models were built for predicting whether a neighborhood will soon see a
large rise in home prices, based on public elementary school ratings and other factors. The
training data set was missing the school rating variable for every new school (3% of the data
points).
Because ratings are unavailable for newly-opened schools, it is believed that locations that
have recently experienced high population growth are more likely to have missing school
rating data.
• Model 1 used imputation, filling in the missing data with the average school
rating from the rest of the data.
• Model 2 used imputation, building a regression model to fill in the missing school
rating data based on other variables.
• Model 3 used imputation, first building a classification model to estimate (based
on other variables) whether a new school is likely to have been built as a result of
recent population growth (or whether it has been built for another purpose, e.g.
to replace a very old school), and then using that classification to select one of two
regression models to fill in an estimate of the school rating; there are two different
regression models (based on other variables), one for neighborhoods with new
schools built due to population growth, and one for neighborhoods with new
schools built for other reasons.
• Model 4 used a binary variable to identify locations with missing information.
• Model 5 used a categorical variable: first, a classification model was used to
estimate whether a new school is likely to have been built as a result of recent
population growth; and then each neighborhood was categorized as "data
available", "missing, population growth", or "missing, other reason".
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