TY - JOUR A1 - Alzaatreh, Ayman A1 - Aljarrah, Mohammad A1 - Almagambetova, Ayanna A1 - Zakiyeva, Nazgul T1 - On the Regression Model for Generalized Normal Distributions JF - Entropy N2 - The traditional linear regression model that assumes normal residuals is applied extensively in engineering and science. However, the normality assumption of the model residuals is often ineffective. This drawback can be overcome by using a generalized normal regression model that assumes a non-normal response. In this paper, we propose regression models based on generalizations of the normal distribution. The proposed regression models can be used effectively in modeling data with a highly skewed response. Furthermore, we study in some details the structural properties of the proposed generalizations of the normal distribution. The maximum likelihood method is used for estimating the parameters of the proposed method. The performance of the maximum likelihood estimators in estimating the distributional parameters is assessed through a small simulation study. Applications to two real datasets are given to illustrate the flexibility and the usefulness of the proposed distributions and their regression models. Y1 - 2021 U6 - https://doi.org/https://doi.org/10.3390/e23020173 VL - 23 IS - 2 SP - 173 ER -