Nonlinear Least Squares Fitting

Nonlinear Least Squares Fitting is a mathematical method used to find the best curve that matches a set of data points when the relationship between variables isn’t a straight line. It involves choosing a model with adjustable parameters, then tweaking those parameters so the model’s predictions are as close as possible to the actual data. The "least squares" part means it minimizes the total squared difference between the data points and the model, ensuring the best overall fit. This technique helps understand complex patterns in data where relationships are curved or more intricate.