Neliösovitusta

Neliösovitusta, also known as least squares fitting, is a mathematical and statistical method used to find the best-fitting curve to a given set of data points. The core idea is to minimize the sum of the squares of the differences between the observed values and the values predicted by the curve. This difference is often referred to as the residual or error.

The most common application of neliösovitusta is in linear regression, where a straight line is fitted to the data. In this case, the goal is to find the slope and intercept of the line that best represents the trend in the data. However, the method can be extended to fit more complex curves, such as polynomials or exponential functions, by adjusting the form of the function being fitted.

Neliösovitusta is widely used in various fields, including science, engineering, economics, and machine learning, for tasks such as data analysis, model building, and prediction. It provides a systematic way to quantify the relationship between variables and to assess the goodness of fit of a model to the data. The method assumes that the errors are independent and have constant variance.