In recent years, birth outcomes have resulted in large economic cost, both direct medical cost and long-term developmental consequences. It is therefore not surprising that governments and cooperate entities focus their attention to prenatal-care improvements. Both low and high birth weights are examined to see the long-term effects on cognitive development, educational outcomes, and labor-market outcomes. However, several data including birth weight data involves the analysis of skewed data. When a distribution of variables is highly skewed, it implies the mean is sensitive to outliers and certainly not a good measure of central tendency. In this case, quantile regression is one approach that is appropriate to analyse such data. The performance of the best quantile regression model is compared with other skewed models such as the Weibull and lognormal regressions. The best regression model that fits the birth weight data is determined using the Q-Q plot and the AIC.
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