Görbeillesztés

Görbeillesztés is a Hungarian term that translates to "curve fitting" in English. It refers to the process of constructing a mathematical function that best represents a series of data points. This function, often called a curve or a model, aims to capture the underlying trend or relationship within the data, even if the data itself is noisy or contains some error.

The primary goal of görbeillesztés is to approximate a set of observed values with a continuous function. This can be useful for various purposes, such as interpolation (estimating values between known data points), extrapolation (estimating values beyond the range of known data points), data smoothing, and understanding the nature of the relationship between variables.

Common methods for görbeillesztés include polynomial regression, where a polynomial function is used to fit the data; linear regression, a special case of polynomial regression with a first-degree polynomial; and more complex non-linear regression techniques for datasets that do not follow a linear or simple polynomial pattern. The choice of method depends on the nature of the data and the desired accuracy of the fit.

The success of görbeillesztés is often evaluated using statistical measures like the coefficient of determination (R-squared) or the root mean squared error (RMSE), which quantify how well the fitted curve represents the original data. In essence, görbeillesztés is a fundamental technique used across many scientific and engineering disciplines for data analysis and modeling.