The Application of Quantile Regression in the Analysis of Influential Factors of Uric Acid

Because only one set of parameter estimates can be obtained using ordinary least squares regression, it is impossible to analyze the effect of different body mass index (BMI) values on uric acid in depth. Therefore, we can only analyze the effect of body mass index on average level of uric acid. In this study, we attempt to build a regression model using quantile regression theory and use the model to analyze the effect of BMI on uric acid. Based on the quantile regression simulation results using sample data of uric acid and BMI collected from adult in Tianjin, we found that different BMI values have different effects on uric acid. In addition, we also obtained uric acid values for people in different genders and people with different BMI values under various tau values using quantile regression.