Quantile regression is a useful tool in understanding complex predictor-response relationships. In 2017, Yang and Tokdar presented a method for joint quantile regression across all quantile levels, opening the way for rigorous analysis of whether and how predictor effects vary between levels. We demonstrate how their method offers a comprehensive and model-based regression analysis framework. We illustrate how to interpret coefficients, improve and compare models, and obtain predictions under this framework. In an ecological application, we analyze how red maple abundance relates to site covariates. A complete absence of the species contributes excess zeros, which we treat as left censored in the spirit of a Tobit regression. By utilizing the generative nature of the joint quantile regression model, we are additionally able to make inference about the probability of the response being zero.