Univariate Linear Regression

Regression is the process of fitting models to data. Linear regression assumes that the relationship between the dependent variable y_i and the independent variable x_i is linear: y_i = a + b x_i. Here a is the offset and b is the slope of the linear relationship.

Univariate Nonlinear Regression

Nonlinear regression can assume any type of relationship between the dependent variable y and independent variables x_j. For nonlinear regression, MuPAD provides the stats::reg function. This function uses the least-squares approach for computing the regression. stats::reg chooses the parameters p₁, ..., p_n by trying to minimize the quadratic error:

Multivariate Regression

The stats::reg function also performs linear and nonlinear regressions with two or more independent variables. The following example demonstrates how to perform regression and fit the data to a function of two variables. Suppose, you have three data lists that contain coordinates x, y, and z of discrete points: