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

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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 [Show More]

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