The article introduces a new way to analyze grouped data without assuming normal distributions. It extends traditional methods by considering different variances for errors and using non-normal distributions. The researchers suggest consistent estimators for model parameters and derive biases and variances. They also provide estimators for mean squared errors and investigate the model through simulations and real-world studies.