ISYE 6501 Week 3 Homework Latest Update

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ISYA 6501 Week 3 Homework Question 5.1 Hide # clear env rm(list = ls()) # import packages library(outliers) set.seed(12) uscrime <- read.table("/Users/wstamatis/OMSA/ISYE 6501/Week 3 Homework/... uscrim e.txt", stringsAsFactors = FALSE, header = TRUE) temps <- read.table("/Users/wstamatis/OMSA/ISYE 6501/Week 3 Homework/temps.tx t", stringsAsFactors = FALSE, header = TRUE) # check for outliers using grubbs.test grubbs.test(uscrime$Crime) Grubbs test for one outlier data: uscrime$Crime G = 2.81290, U = 0.82426, p-value = 0.07887 alternative hypothesis: highest value 1993 is an outlier Hide # verify with a five-number summary summary(uscrime$Crime) Min. 1st Qu. Median Mean 3rd Qu. Max. This study source was downloaded by 100000839632511 from CourseHero.com on 05-13-2022 05:49:39 GMT -05:00 https://www.coursehero.com/file/39779415/ISYE6501-Homework-3-Solutionpdf/ 342.0 658.5 831.0 905.1 1057.5 1993.0 Hide # visualize plot(uscrime$Crime) Hide uscrime$Crime[0:10] [1] 791 1635 578 1969 1234 682 963 1555 856 705 We see that 1993 is the clearest outlier, with 1969 being a close second. Question 6.1 I work for an EdTech platform, and tracking active users is a very important metric for us. We want to be able to reach out to schools that aren’t using our product BEFORE we lose them, and applying the CUSUM approach we can detect schools that are at risk of dropping and reach out to them. To be honest, I would experiment a little to find an accurate critical value and threshold. To start out, I would choose a relatively critical value and low threshold. I would gradually increase the threshold until we noticed that we actually started losing accounts, at which point I would adjust to find some stability. [Show More]

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