A linear regression model assumes that the regression function is linear in the inputs. Namely, for a system with n datapoints and p parameters, it can be represented by this matrix equation

\mathbf y = \mathbf X\mathbf \beta + \mathbf \epsilon ,

where \mathbf X is the n\times p input matrix, \mathbf y is the output vector, \mathbf \beta is the vector of regression coefficients and \mathbf \epsilon contains the errors.

Some methods of linear regression I've written about:

11:42 Wednesday 26 May 2021