Quantile regression is a statistical technique that models the relationship between variables for different quantiles of the conditional distribution of the dependent variable, not just the mean. Unlike traditional linear regression, which estimates the conditional mean, quantile regression estimates parameters for specific quantiles. This provides a more detailed view of how the relationship between variables changes at different points in the distribution, which is useful in the presence of outliers or skewed distributions. The technique minimizes a weighted loss function based on the desired quantile, thus offering better robustness. In this paper, a quantile regression model is used to explore the relationship between sex, age, and educational level at different points in the distribution of total monthly income. Using the permanent household survey database of the National Institute of Statistics of Honduras (INE), the model results demonstrate that the effects of these variables vary significantly between different quantiles of the income distribution.