calculate linear regression in matlab

To calculate linear regression in MATLAB, you can use the regress function, which performs a multiple regression analysis. Here is an example:

main.m
% Generate some sample data
x = 1:10;
y = 2.*x + 3 + randn(1,length(x));

% Perform linear regression
X = [ones(length(x),1) x'];
[b,bint,r,rint,stats] = regress(y',X);

% Print the results
fprintf('Slope: %.4f\n', b(2));
fprintf('Intercept: %.4f\n', b(1));
fprintf('R-squared: %.4f\n', stats(1));
fprintf('Standard error of estimate: %.4f\n', stats(4));
355 chars
14 lines

In this example, we generate some sample data (x and y), and then use the regress function to perform the linear regression analysis. The X matrix contains the predictor variables (in this case, a column of ones for the intercept and a column of x values), and we pass in the response variable (y). The output variables (b, bint, r, rint, and stats) contain various pieces of information about the regression analysis. We then print out the slope, intercept, R-squared value, and standard error of estimate.