matlab regress

The function `regress` in MATLAB is used for linear regression analysis. It takes two input arguments, the dependent variable and the independent variable(s), and returns the coefficients of a linear regression model.

Syntax:

`[b, r, r_sq] = regress(y, X)`

where:
- `y`: dependent variable (response)
- `X`: independent variable(s) (predictor)
- `b`: estimated coefficients of the linear regression model
- `r`: residuals (deviations of actual values from predicted values)
- `r_sq`: coefficient of determination (R-squared) - a measure of how well the model fits the data

Example:

Suppose we have the following data:

x = [1 2 3 4 5];
y = [1.5 3.5 5.5 7.5 9.5];

To perform linear regression analysis and obtain the coefficients of the regression model, we can use the `regress` function as follows:

X = [ones(length(x),1), x'];
[b, r, r_sq] = regress(y', X);

The resulting coefficients `b` are:

b = 
    1.0000
    2.0000

This means that the regression model is: `y = 1 + 2x`. The R-squared value `r_sq` is 1, which indicates that the model fits the data perfectly.