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Least Squares Method of Linear Regression

Clear and Complete - WITH LOTS OF SOLVED PROBLEMS

# Least Squares Method of Linear Regression

The Least Square Method is a commonly-used method to solve for linear regression equations. The least squares method is a data fitting method which determines the linear regression equation that produces the lowest sum of the squares of residual values.

A residual value is the difference between the actual (observed) Y value and the Y value calculated by the linear regression equation. The least squares method is an iterative process than continuously recalculates the linear regression equation until the sum of the squares of all residuals has been minimized.

Generally speaking, the least squares method can be used to solve for systems in which there is a set of equations that has fewer unknowns than equations.

The Least Squares Method is an iterative method that can be applied, for example, with the Excel Solver. The Excel Solver can be easily configured to determine the coefficients and Y-intercept of the linear regression line that minimizes the sum of the squares of all residuals of each input equation.

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