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650-project

BIOSTATISTICS 650 GROUP PROJECT Analysis of the National Health and Nutrition Examination Survey Data Background: The National Health and Nutrition Examination Survey (NHANES) program at the Centers for Disease Control and Prevention (CDC) is a cross-sectional survey study designed to assess the health and nutritional status of adults and children in the United States. It began in the 1960s and since 1999 the survey moved to continuous data collection in two-year cycles. Approximately 5,000 individuals of all ages are interviewed in their homes every year and complete the health examination component of the survey. The sample for each two-year cycle is representative of the NHANES target population, which is the non-institutionalized civilian resident population of the United States. NHANES is used to determine the prevalence of major diseases and risk factors for diseases, as well as to assess nutritional status and its association with health promotion and disease prevention. NHANES findings are also the basis for national standards for measurements such as height, weight, and blood pressure. You can read more about NHANES on the CDC’s website. The CDC uses sampling strategies (2007-2010, 2011-2014) to purposefully oversample certain subpopulations like racial minorities. Naive analysis of the original NHANES data can lead to mistaken conclusions because the percentages of people from each racial group in the data, for example, are quite different from the way they are in the target population. In this project, you will use the “NHANES” data from the R package “NHANES”. Please refer to this file for variable definitions and usage of the package/data. This dataset contains 10,000 individuals resampled from the original 2009-2010 and 2011-2012 NHANES data and can be treated as if it were a simple random sample from the US population. Please investigate the association between X (predictor of interest) and Y (outcome of interest). You will determine what X and Y are, as well as your own scientific question(s) of interest. Note that the outcome of interest can be either continuous or discrete as long as you can justify why using linear regression is appropriate for answering your scientific question.

learning-sas-by-example-2nd

This repository contains the sample code for the book Learning SAS by Example: A Programmer's Guide, Second Edition

python3-cookbook

《Python Cookbook》 3rd Edition Translation

readme

sql-practice