Using a Linear Regression to Estimate the Average BMI of Individuals Aged 12–20

Presented by:
Mikaela Schultz (Elon University)

This study used linear regression to examine the relationship between BMI and internal and external variables of those aged 12-20. While BMI does not directly measure body fat, research indicates that BMI correlates to direct measures of body fat that indicates whether a person is underweight, normal weight, overweight, or obese. Data from the IPUMS (Integrated Public Use Microdata Series) website was collected, imported to R, and cleaned to create a final model that can be used to analyze trends in average BMIs among different groups of individuals. A collection of predictor variables were initially chosen to create a large linear regression model. From these variables, backward elimination methods (along with considerations for redundant information) were used to create a smaller model that included age, sex, race and poverty level as important predictors of BMI. Currently, an R-Shiny app is being created based on this model to allow interactive visualization of these findings.