Intro Analytics Modeling ISYE 6501 O01 OAN O01 MSA

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Intro Analytics Modeling ISYE 6501 O01 OAN O01 MSA INSTRUCTIONS FOR QUESTIONS 1-5 For each of the following five questions, select the probability distribution that could best be used to model th... e 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). Move To... This element is a more accessible alternative to drag & drop reordering. Press Enter or Space to move this question. Question 1 1.4 / 1.4 pts Number of people clicking an online banner ad each hour Binomial Exponential Geometric Correct! Poisson Weibull Move To... This element is a more accessible alternative to drag & drop reordering. Press Enter or Space to move this question. Question 2 1.4 / 1.4 pts Time from when a generator is turned on until it fails Binomial Exponential Geometric Poisson Correct! Weibull Move To... This element is a more accessible alternative to drag & drop reordering. Press Enter or Space to move this question. Question 3 1.4 / 1.4 pts Number of hits to a real estate web site each minute Binomial Exponential Geometric Correct! Poisson WeibullMove To... This element is a more accessible alternative to drag & drop reordering. Press Enter or Space to move this question. Question 4 1.4 / 1.4 pts Number of people entering a grocery store each minute Binomial Exponential Geometric Correct! Poisson Weibull Move To... This element is a more accessible alternative to drag & drop reordering. Press Enter or Space to move this question. Question 5 1.4 / 1.4 pts Time between hits on a real estate web site Binomial Correct! Exponential GeometricPoisson Weibull Move To... This element is a more accessible alternative to drag & drop reordering. Press Enter or Space 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" [Show More]

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