The simultaneous mean-variance regression method improves predictions by considering varying dispersion in data, even when the form of heteroskedasticity is unknown. This approach accounts for both the average outcome and the variability in the data, leading to more accurate predictions compared to traditional methods like ordinary least squares. The method also provides valid parameter standard errors automatically. The researchers demonstrated the effectiveness of this method through numerical simulations and real-world applications, such as estimating the relationship between economic prosperity in the past and present, and predicting gasoline demand in the United States.