The method of least squares is a standard approach in regression analysis for the purpose of fitting data.
The goal of this method is to minimize the sum of the squares of the errors as much as possible.
Least squares problems fall into two categories:
- Linear or ordinary least squares which occurs in statistical regression analysis.
- Nonlinear least squares.
These are categorized into ordinary least squares, weighted least squares, alternating least squares, and partial least squares.
A least-squares regression line fits a linear relationship between two variables by minimizing the vertical distance between the data points and the regression line.
Since it is the minimum value of the sum of the squares of the errors, it is also known as the variance.
Regression and evaluation make extensive use of the method of least squares.
This method is used as a solution to minimize the sum of the squares of all the deviations produced by each equation.
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