Midterm Quiz 2 analytics modeling

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): 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 change it. There is no overall Submit button. Information for Question 1 There are five questions labeled "Question 1." Answer all five questions. 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. Question 1 0.0/1.4 points (graded) Time between people clicking an online banner ad Answer: Exponential You have used 1 of 1 attempt Answers are displayed within the problem Question 1 0.0/1.4 points (graded) Number of people clicking an online banner ad each hour Answer: Poisson You have used 1 of 1 attempt Answers are displayed within the problem Question 1 0.0/1.4 points (graded) Time from when a house is put on the market until the first offer is received Answer: Weibull You have used 1 of 1 attempt Answers are displayed within the problem Question 1 0.0/1.4 points (graded) Number of hits to a real estate web site each minute Answer: Poisson You have used 1 of 1 attempt Answers are displayed within the problem Question 1 0.0/1.4 points (graded) Time from the start of a World Cup soccer match until a goal is scored Answer: Weibull You have used 1 of 1 attempt Answers are displayed within the problem Questions 2a, 2b 5.0/10.0 points (graded) 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". 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-classified using the other factors, which model would you recommend? Model 1 Model 2 Model 3