Multiple linear regression models and their application in the analysis of cardiovascular variables in university students from Southern Sonora

F. Legarreta Muela, Julián Esparza, R. Terminel Zaragoza, Toledo Domínguez, Quinero Portillo H., Ulloa Mercado R., Gortáres Moroyoqui P., Meza Escalante E. and A Rentería Mexía
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F. Legarreta Muela: Instuto Tecnológico de Sonora
Julián Esparza: Instuto Tecnológico de Sonora
R. Terminel Zaragoza: Instuto Tecnológico de Sonora
Toledo Domínguez: Instuto Tecnológico de Sonora
Quinero Portillo H.: Instuto Tecnológico de Sonora
Ulloa Mercado R.: Instuto Tecnológico de Sonora
Gortáres Moroyoqui P.: Instuto Tecnológico de Sonora
Meza Escalante E.: Instuto Tecnológico de Sonora
A Rentería Mexía: Instuto Tecnológico de Sonora

Mexican Stata Conference 2023 from Stata Users Group

Abstract: Multiple linear regression is one of the most important statistical techniques used in nutrition epidemiology to analyze the predictive effect of exposure variables on a response variable, which should be quantitative. Variables identified with the potential to be modifiable can in turn be used in preventive programs. The objective of this research was to analyze the association between behavioral variables related to cardiovascular health with anthropometric indicators of obesity in freshman university students enrolled at the Technological Institute of Sonora. The response variable was body fat, and the predictor variables were food and nutrient groups and physical activity, according to the criteria of the American Heart Association. Potential association analyses were used, and multiple models were built by stepwise forward selection (p≤0.05 and biological plausibility) with data from 230 university adolescents using the Stata software.

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