Midt.erm Q.uiz 2 - GT Students and Verified (COMSCI183)
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Midterm Quiz 2 - GT Students and Verified MM Learners |
5/1/2020
GTx: ISYE6501x
Introduction to Analytics Modeling Help khoi32
Midterm Quiz 2 | ISYE6501x Courseware | edX
Midterm Quiz 2 - GT Students and
Veri�ed MM Learners
Course Midterm Quiz 2 - Spring 2020 Midterm
Quiz 2
Midterm Quiz 2 - GT Students and Veri�ed MM Learners
90 Minute Time Limit
Instructions
Work alone. Do not collaborate with or copy from anyone
else. You may use any of the following resources:
One sheet (both sides) of handwritten (not photocopied or scanned) notes
If any question seems ambiguous, use the most reasonable interpretation (i.e. don't be like Calvin):
Good Luck!
This the beginning of Midterm Quiz 2. 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 �ve questions labeled "Question 1." Answer all �ve questions. For each of the following �ve 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
�ve questions.
Question 1
1.4/1.4 points (graded)
Number of days in a year where the temperature is more than 3 degrees higher than forecast
Binomial
Submit You have used 1 of 1 attempt
Question 1
1.4/1.4 points (graded)
Number of phone calls made by a telemarketer until one is answered
Geometric
Submit You have used 1 of 1 attempt
Question 1
1.4/1.4 points (graded)
Time from the beginning of Fall until the �rst snow�ake is seen
Weibull
Submit You have used 1 of 1 attempt
Question 1
1.4/1.4 points (graded)
Time from when a house is put on the market until the �rst o�er is received
, Midterm Quiz 2 - GT Students and Verified MM Learners |
5/1/2020
Midterm Quiz 2 | ISYE6501x Courseware | edX
1.4/1.4 points (graded)
Time from when a generator is turned on until it fails
Weibull
Submit You have used 1 of 1 attempt
Questions 2a, 2b
5.0/10.0 points (graded)
Five classi�cation 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, �lling in the missing data with the average school rating from the rest of the data.
Model 2 used imputation, building a regression model to �ll in the missing school rating data based on other variables.
Model 3 used imputation, �rst building a classi�cation 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 classi�cation to select one of two regression models to �ll
in an estimate of the school rating; there are two di�erent 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: �rst, a classi�cation 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".
a. If school ratings cannot be reasonably well-predicted from the other factors, and new schools built due to recent
population growth cannot be reasonably well-classi�ed using the other factors, which model would you recommend?
Model 1
Model 2
Model 3
Model 4
Model 5
b. In which of the following situations would you recommend using Model 5? [All predictions and classi�cations below are
using the other factors.]
Ratings can be well-predicted, and reasons for building schools can be well-classi�ed.
Ratings can be well-predicted, and reasons for building schools cannot be well-classi�ed.
Ratings cannot be well-predicted, and reasons for building schools can be well-classi�ed.
Ratings cannot be well-predicted, and reasons for building schools cannot be well-classi�ed.
Submit You have used 1 of 1 attempt
Answers are displayed within the problem
Information for Question 3
In a diet problem (like we saw in the lessons and homework), let xibe the amount of food i in the solution (x i >= 0), and let
M be the maximum amount that can be eaten of any food.
Suppose we added new variables yithat are binary (i.e., they must be either 0 or 1): if food i is eaten in the solution, then it
is part of the solution (yi = 1); otherwise yi = 0.
There are �ve questions labeled "Question 3." Answer all �ve questions. For each of the following �ve questions, select the
mathematical constraint that best corresponds to the English sentence. Each constraint might be used, zero, one, or more
than one time in the �ve questions.
Question 3
1.4/1.4 points (graded)
Select the mathematical constraint that corresponds to the following English sentence:
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