Linear regression fits a data model that is linear in the model coefficients. The most common type of linear regression is a least-squares fit, which can fit both lines and polynomials, among …

Perform least-squares fitting by using error distributions and linear, weighted, robust, and nonlinear least squares.

Linear least-squares solves min|| C * x - d || 2, possibly with bounds or linear constraints. See Linear Least Squares. Nonlinear least-squares solves min (∑|| F (xi) – yi || 2), where F (xi) is a …

Nonnegative Linear Least Squares, Problem-Based Shows how to solve a nonnegative linear least-squares problem using the problem-based approach and several solvers.

This MATLAB function attempts to solve the system of linear equations A*x = b for x using the Least Squares Method.

To formulate a least-squares problem, follow the instructions in Write Objective Function for Problem-Based Least Squares. For the problem-based steps to take, see Problem-Based …

This MATLAB function returns the coefficients for a polynomial p (x) of degree n that is a best fit (in a least-squares sense) for the data in y.

For linear least squares without constraints, the problem is to come up with a least-squares solution to the problem Cx = d. You can solve this problem with mldivide or lsqminnorm.

This example shows how to use nondefault options for linear least squares. Set options to use the 'interior-point' algorithm and to give iterative display.

You can reduce outlier effects in linear regression models by using robust linear regression. This topic defines robust regression, shows how to use it to fit a linear model, and compares the …