Analysis of Quantile Regression as an Alternative to Multiple Linear Regression: (A Case Study of Birth Weight Data)

The study examine the Quantile Regression (QR) and Multiple Regression (MR) using birth-weight data. The data was first subjected to heteroskedasticity test, and it was concluded that there is existence of heteroskedasticity in the data for the study. The result of fitting an Ordinary Least Square (OLS) regression as well as the result from the QR shows that the estimated coefficient for the gestation positive and has significant impact of this variable on birth weight. The multiple regression model of four explanatory variables, viz: Mother age, Parity, Gestation and maternal height and the response variable (Baby Weight) was first fitted with the coefficient of determination of 65.58%. The test on the significance of the parameters for the multiple regression revealed that all the predictor variables are significant at 5% level of significance, except parity. This means that gestation, mother's age, and maternal height contributed significantly to the baby weight.