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.