HW_Final_20231216

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Stevens Institute Of Technology *

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500

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Economics

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Jan 9, 2024

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You can use Word, Excel, Power Point and SAS to answer the questions in this exam. There are a total of four (4) multi-part questions, with point values noted for each question. Please show your calculations, or the details of your program(s) for each problem. The SAS programs should be commented so that each step is clearly explained. Redirect your SAS output to word (RTF) or pdf file(s) Combine all your answers/files into a single zipped file and post the zipped file to “HW_Final” in CANVAS. Problem #1 (25 points) The SAS “Walmart_montly_22” dataset contains the monthly sales for a Walmart store. Redirect your SAS output to an “RTF” file and use the following methods to forecast the monthly sales for Department 22 for 6 months.  Exponential Smoothing  Double Exponential Smoothing  Multi-seasonal (MULTSEASONAL) Which method is the best? Why? Problem #2 (25 points) Calculate the page rank of the following network. A F E D C B G

Problem #3 (25 points) Use the following utilities to perform the following analysis. Utilities Table Based on the Usual Degrees of Freedom Label Utility Variable Intercept 7.5309 Intercept Dest Beach 0.5556 Class.DestBeach Dest City -0.5556 Class.DestCity activity City Tours 0.3704 Class.activityCity_Tours activity Hiking -0.3704 Class.activityHiking Accom Resort 0.6173 Class.AccomResort Accom _Cabin -0.1235 Class.Accom_Cabin Accom __Hotel -0.4938 Class.Accom__Hotel Price 10k 0.9259 Class.Price10k Price 15K 0.0000 Class.Price15K Price 20k -0.9259 Class.Price20k a) Tradeoff between price and accommodation: What should be the price of the Resort accommodation in the following offerings? Destination Activity Accommodation Price Beach Hiking Resort ? Beach Hiking Cabin 15K b) Market Share forecast: What is the Market Share forecast for each of the following? Destination Activity Accommodation Price Beach Hiking Resort 15K Beach City Tours Hotel 20K Beach Hiking Cabin 10K c) Attribute importance: What is the importance of each feature?

Problem #4 (25 points) An owner of a successful restaurant in Hoboken has decided to open a new restaurant in Manhattan, NY . To find the best location of the new restaurant, the owner has combined the income demographics of Hoboken residents with the income demographics of all the zip codes in Manhattan in a SAS dataset (“Man_HB_zip” ). To recommend the best zip codes to be considered perform the following analysis. a) Cluster the zip codes in the datasets into fifteen zip codes using hierarchical clustering and complete linkage and recommend the appropriate zip code(s). b) Cluster the zip codes in the datasets into fifteen zip codes using Kmeans and recommend the appropriate zip codes. Data dependency: Walmart_montly_22, Man_HB_zip sas dataset

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