Data fitting is a powerful technique that allows to uncover patterns in observed data and use them to make predictions about future trends . At its core, it involves constructing a mathematical model or function that accurately represents the relationship between variables by determining the appropriate parameters . This approach not only simplifies complex data relationships but also serves as a foundation for more advanced modeling techniques, such as polynomial regression and machine learning. The pattern of thermodynamic data for alkane combustion reaction could be fitted to linear equations which is easy to use for any kind of data handling. Figure presents linear fits for reaction enthalpy as a function of the number of carbon atoms (n) in alkanes using DFT functionals with 6-31G(d) basis set. Each subplot shows the linear equation and corresponding R-square value, which is equal to 1.00 across all methods, confirming an excellent linear correlation between the number of carbon atoms and the computed reaction enthalpy. The slope of each linear fit represents the enthalpy change per additional carbon atom in the alkane chain.