The article introduces two new methods for analyzing errors in scientific models related to aerosol dynamics and climate simulation. These methods, called compound regression and constrained regression, are shown to be equivalent to each other and to traditional modeling approaches. They have advantages like being easy to understand visually, not relying on specific distributions, and being useful when certain error variances are unknown. These methods can also be applied to multiple linear regression with more than one variable.