Georgia Institute Of TechnologyISYE 6501Midterm Quiz 2 - GT Students and Verified MM Learners _ Midterm Quiz 2 _ ISYE6501x Courseware _ edX.

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Midterm Quiz 2 - GT Students and VeriÕed 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: O... ne 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   4/6/2020 Midterm Quiz 2 - GT Students and Verified MM Learners | Midterm Quiz 2 | ISYE6501x Courseware | edX https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+1T2020/courseware/f712bb2a96ff46b0bc8d775293bfc91d/1b57ff6ea64c40cf8f4eb69d2b… 2/26 change it. There is no overall Submit button. Information for Question 1 There are Õve questions labeled "Question 1." Answer all Õve questions. For each of the following Õve 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 Õve questions. Question 1 0.0/1.4 points (graded) Number of people clicking an online banner ad each hour Exponential  You have used 1 of 1 attempt Question 1 0.0/1.4 points (graded) Time between people clicking an online banner ad Weibull  You have used 1 of 1 attempt Question 1 0.0/1.4 points (graded) Time from when a house is put on the market until the Õrst oàer is received Poisson  You have used 1 of 1 attempt Submit Submit Submit4/6/2020 Midterm Quiz 2 - GT Students and Verified MM Learners | Midterm Quiz 2 | ISYE6501x Courseware | edX https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+1T2020/courseware/f712bb2a96ff46b0bc8d775293bfc91d/1b57ff6ea64c40cf8f4eb69d2b… 3/26 Question 1 0.0/1.4 points (graded) Time from the start of a World Cup soccer match until a goal is scored Poisson  You have used 1 of 1 attempt Question 1 0.0/1.4 points (graded) Number of hits to a real estate web site each minute Binomial  You have used 1 of 1 attempt Questions 2a, 2b 5.0/10.0 points (graded) 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 Submit Submit4/6/2020 Midterm Quiz 2 - GT Students and Verified MM Learners | Midterm Quiz 2 | ISYE6501x Courseware | edX https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+1T2020/courseware/f712bb2a96ff46b0bc8d775293bfc91d/1b57ff6ea64c40cf8f4eb69d2b… 4/26 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 can 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 2? [All predictions and classiÕcations below are using the other factors.] Model 1 Model 2 Model 3 Model 4 Model 5 Ratings can be well-predicted, and reasons for building schools can be wellclassiÕed. Ratings can be well-predicted, and reasons for building schools cannot be wellclassiÕed.4/6/2020 Midterm Quiz 2 - GT Students and Verified MM Learners | Midterm Quiz 2 | ISYE6501x Courseware | edX https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+1T2020/courseware/f712bb2a96ff46b0bc8d775293bfc91d/1b57ff6ea64c40cf8f4eb69d2b… 5/26  You have used 1 of 1 attempt 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: Either cheese sauce or peanut butter (or both) must be eaten with broccoli. Ratings cannot be well-predicted, and reasons for building schools can be wellclassiÕed. Ratings cannot be well-predicted, and reasons for building schools cannot be wellclassiÕed. [Show More]

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