Least Squares Modeling of Surface Tension of Alkanes

We introduce a straight forward and effective method for analyzing data through linear least squares fitting using the popular Maple program. The least squares method serves as a technique to determine the best-fit line for a specific dataset. Surface tension of n-alkanes is commonly represented as a function of temperature and can be correlated efficiently using least-squares regression. This approach streamlines data analysis and improves the accuracy of predictions. The basic problem is to find the best fit straight line y = A + B.x given that for n ∈ {1,…. N} for the observed experimental data in pairs (x_n, y_n). Each point of data represents the relationship between a known independent variable and an unknown dependent variable. The lower standard error of the estimate indicates better fit for the model.