Homework 4 In this assignment, you will need to mine the 2011-2012 NHANES data using Enterprise Miner. Due: June 25 2020 Data Background of the NHANES data: The National Health and Nutrition Examination Survey (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The survey is unique in that it combines interviews and physical examinations. NHANES is a major program of the National Center for Health Statistics (NCHS). The NHANES program began in the early 1960s and has been conducted as a series of surveys focusing on different population groups or health topics. In 1999, the survey became a continuous program that has a changing focus on a variety of health and nutrition measurements to meet emerging needs. The survey examines a nationally representative sample of about 5,000 persons each year. These persons are located in counties across the country, 15 of which are visited each year. The NHANES interview includes demographic, socioeconomic, dietary, and health-related questions. The examination component consists of medical, dental, and physiological measurements, as well as laboratory tests administered by highly trained medical personnel. Source: NHANES 2011-12 NHANES Data Download Two data files NHANES 2011-2012 Demographics Data NHANES 2011-2012 Questionnaire Data - Food Insecurity Save both the doc file and the data files (DEMO_G.XPT. FSQ_G.XPT) in your local drive. Instruction of the Assignment 1. Import two data file to Enterprise Miner. To import .xpt file to enterprise miner, follow these instructions: a. Cicik on the project b. On the project property panel, click on project start code c. type the following: libname HW4 'C:\\xxxxx' ; /* Where xxxxx is the directory where you saved your .xpt file*/ libname xptfile xport "C:\\XXXXX\\DEMO_G.XPT" access=readonly; proc copy inlibexptfile outlib=aw4; run ; libname xptfile xport "C:\\XXXXX\\FSQ_G.XFT" access=readonly; proc copy inlib=xptfile outlib=HW4; run; d. Highlight the code and click run now. 2. Create two data sources - one for Demo_g and one for FSQ_G 3. Do the following: On demo_g a. set AlALANGA to be "rejected" b. set SEON to be "ID" ON fog 9 a. Set Target variable to be "FSDHH" and make it ordinal b. Set the following to be rejected:FSDAD, FSDCH c. Set SEON to be "ID" 4. In the sample tab, click on "Merge", set "Merging" propery to be "Match" 5. Explore the variables and preprocess them wherever appropriate. 6. Build decision tree, logistic regression, and the ensemble models 7. Assess the models and select the best model 8. Produce a PDF report of the model assessment and submit the report on the blackboard.

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