ISYE-6501-OAN/O01 -- HOMEWORK 9 – SOLUTIONS 12.1, Questions and answers, 100% Accurate.

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ISYE-6501-OAN/O01 -- HOMEWORK 9 – SOLUTIONS 12.1 As a software engineer of a service-oriented company, we try to upsell many products to the customers and want to display the ads of a product/ser... vice within the mobile application. Most of the time we don’t have much idea about the right screen/position of the ad, so it catches most eyeballs and company can end up selling that product/service. In that case, we use experiment approach such as A-B testing to place the ad in separate locations and gather the analytics to better understand the most valuable place to post the ad. 12.2 if (!require("FrF2")) install.packages('FrF2', repos='http://cran.us.r-project.org') ## Loading required package: FrF2 ## Loading required package: DoE.base ## Loading required package: grid ## Loading required package: conf.design ## ## Attaching package: 'DoE.base' ## The following objects are masked from 'package:stats': ## ## aov, lm ## The following object is masked from 'package:graphics': ## ## plot.design ## The following object is masked from 'package:base': ## ## lengths set.seed(42) combo <- FrF2(16,10) combo This study source was downloaded by 100000834091502 from CourseHero.com on 05-16-2022 06:23:47 GMT -05:00 https://www.coursehero.com/file/69470887/ISYE-6501-hw-9pdf/Output— The explanation is fairly simple - each of the factors from A through are binary variables. We have 16 combinations of which factors to keep. If we look at the first combination, we keep {B, C, G, H, K}. The combinations were chosen to never repeat between the 16, and also to try to have the most variability in interactions. For example, both A and B are included only in the 15th and 16th combinations. Considering that there are 2^10 = 1024 possible combinations, we have some overlap with other variables. This study source was downloaded by 100000834091502 from CourseHero.com on 05-16-2022 06:23:47 GMT -05:00 https://www.coursehero.com/file/69470887/ISYE-6501-hw-9pdf/13.1 a. Binomial Whether or not a movie will profit - basically, we can think of any binary question for the binomial distributions that a model such as logistic regression may solve. b. Geometric At which day after release that the movie will break even - this may never happen since the movie may never break even, just like how a bat may never break during the experiment held by the class lecture example. However, outside of that, we can try to see when a specific production’s movies tend to break even and find probability parameters based off that. c. Poisson The probability of a particular movie ticket (the golden ticket) being bought. d. Exponential How many regular tickets are bought before each of the several golden tickets being found. e. Weibull How long until a golden ticket is boug [Show More]

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