Question 1
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Number of days in a year where the temperature is more than 3 degrees higher than forecast
Binomial
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Question 1
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Number of phone calls made by a telemarketer until one is answered
Geometric
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Question 1
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Time from the beginning of Fall until the Õrst snowÖake is seen
Weibull
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Question 1
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Time from when a house is put on the market until the Õrst oàer is received
Weibull
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Question 1
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Submit5/1/2020 Midterm Quiz 2 - GT Students and Verified MM Learners | Midterm Quiz 2 | ISYE6501x Courseware | edX
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1.4/1.4 points (graded)
Time from when a generator is turned on until it fails
Weibull
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Questions 2a, 2b
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Five classiÕcation 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, Õlling in the missing data with the average school rating from the rest of the data.
Model 2 used imputation, building a regression model to Õll in the missing school rating data based on other variables.
Model 3 used imputation, Õrst building a classiÕcation 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 classiÕcation to select one of two regression models to Õll in an estimate of the school rating; there are two diàerent
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: Õrst, a classiÕcation 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-classiÕed using the other factors, which model would you recommend?
b. In which of the following situations would you recommend using Model 5? [All predictions and classiÕcations below are using the other
factors.]
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Information for Question 3
In a diet problem (like we saw in the lessons and homework), let x be the amount of food i in the solution (x >= 0), and let M be the
maximum amount that can be eaten of any food.
Suppose we added new variables y that 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 (y = 1); otherwise y = 0.
There are Õve questions labeled "Question 3." Answer all Õve questions. For each of the following Õve 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 Õve questions.
Question 3
1.4/1.4 points (graded)
Select the mathematical constraint that corresponds to the following English sentence:
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Model 1
Model 2
Model 3
Model 4
Model 5
Ratings can be well-predicted, and reasons for building schools can be well-classiÕed.
Ratings can be well-predicted, and reasons for building schools cannot be well-classiÕed.
Ratings cannot be well-predicted, and reasons for building schools can be well-classiÕed.
Ratings cannot be well-predicted, and reasons for building schools cannot be well-classiÕed.
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