Intro Analytics Modeling ISYE 6501 OAN O01 QCH A

Intro Analytics Modeling ISYE 6501 OAN O01 QCH A Attempt History Attempt Time Score LATEST Attempt 1 24 minutes 86.02 out of 100.02 Score for this quiz: 86.02 out of 100.02 Submitted Jul 11 at 3:25pm This attempt ...

Intro Analytics Modeling ISYE 6501 OAN O01 QCH A Attempt History Attempt Time Score LATEST Attempt 1 24 minutes 86.02 out of 100.02 Score for this quiz: 86.02 out of 100.02 Submitted Jul 11 at 3:25pm This attempt took 24 minutes. 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):7/26/2021 ISYE 6501 Midterm 2 : Intro Analytics Modeling - ISYE-6501-OAN/O01/QCH/A https://gatech.instructure.com/courses/188884/quizzes/254129?module_item_id=1611528 4/34 If you experience any technical issues (i.e. images not loading) you may refresh the page without interrupting your exam attempt. If the issue persists, then please finish the exam and let the Instructors know about the issue in a private Piazza post afterwards. Good Luck! INSTRUCTIONS FOR QUESTIONS 1-5 For each of the following five 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 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). Question 1 1.4 / 1.4 pts Number of people clicking an online banner ad each hour Binomial7/26/2021 ISYE 6501 Midterm 2 : Intro Analytics Modeling - ISYE-6501-OAN/O01/QCH/A Exponential Geometric Correct! Correct! Poisson Weibull Question 2 1.4 / 1.4 pts Time from the beginning of Fall until the first snowflake is seen Binomial Exponential Geometric Poisson Correct! Correct! Weibull Question 3 1.4 / 1.4 pts Number of arrivals to a flu shot clinic each minute Binomial Exponential Geometric7/26/2021 ISYE 6501 Midterm 2 : Intro Analytics Modeling - ISYE-6501-OAN/O01/QCH/A Correct! Correct! Poisson Weibull Question 4 1.4 / 1.4 pts Time between hits on a real estate web site Binomial Correct! Correct! Exponential Geometric Poisson Weibull Question 5 1.4 / 1.4 pts Number of arrivals to the ID-check queue at an airport each minute Binomial Exponential Geometric Correct! Correct! Poisson Weibull7/26/2021 ISYE 6501 Midterm 2 : Intro Analytics Modeling - ISYE-6501-OAN/O01/QCH/A https://gatech.instructure.com/courses/188884/quizzes/254129?module_item_id=1611528 7/34 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". Question 6 5 / 5 pts7/26/2021 ISYE 6501 Midterm 2 : Intro Analytics Modeling - ISYE-6501-OAN/O01/QCH/A https://gatech.instructure.com/courses/188884/quizzes/254129?module_item_id=1611528 8/34 If school ratings can be reasonably well-predicted from the other factors, and new schools built due to recent population growth cannot be reasonably well-classified using the other factors, which model would you recommend? Model 1 Correct! Correct! Model 2 Model 3 Model 4 Model 5 Question 7 0 / 5 pts In which of the following situations would you recommend using Model 3? Ratings can be well-predicted, and reasons for building schools can be well-classified orrect Answer orrect Answer Ratings can be well-predicted, and reasons for building schools cannot be well-classified ou Answered ou Answered Ratings cannot be well-predicted, and reasons for building schools can be well-classified Ratings cannot be well-predicted, and reasons for building schools cannot be well-classified