n = 40;
xmin = 2.2;
xmax = 3.9;
c = 6.0;
noise = 50.0;

% generate data:
x = rand(n, 1) * (xmax-xmin) + xmin;
yest = c * x.^3;
y = yest + noise*randn(n, 1);

% compute mean squared error:
mse = mean((y - y_est).^2);

fprintf('the mean squared error is %g kg^2\n', mse))