Understanding data fitting with ANNs

Data fitting is the process of constructing a curve or mathematical function that best matches a given set of collected data points. This curve fitting can involve either interpolations, where exact data points are fitted, or smoothing, where a smooth function approximates the data. In the context of regression analysis, curve fitting is closely related to statistical inference, considering uncertainties arising from random errors in observed data.

The approximate curves obtained through data fitting have multiple applications. They can be used to visualize and display the data, predict function values in regions with no available data, and summarize the relationships between multiple variables. This process is valuable for understanding and interpreting complex datasets, making predictions, and gaining insights from the collected information.

Predicting the trend of a particular distribution using mathematical formulas can be challenging...