polynomial least squares in matlab

To perform polynomial least squares regression in Matlab, you can use the built-in function polyfit. Here's an example:

main.m
% Generate some sample data
x = linspace(-3, 3, 100)';
y = x.^3 - 2*x.^2 + x + randn(size(x));

% Set the degree of the polynomial
n = 3;

% Fit the polynomial using least squares regression
p = polyfit(x, y, n);

% Evaluate the polynomial at a finer grid of points
xfine = linspace(-3, 3, 1000)';
yfit = polyval(p, xfine);

% Plot the data and the fitted polynomial
plot(x, y, 'o', xfine, yfit, '-')
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In this example, we generate some sample data, then use polyfit to fit a cubic polynomial to the data using least squares regression. The resulting polynomial coefficients are stored in the variable p. We then evaluate the polynomial at a finer grid of points and plot the results.

Note that the third argument to polyfit specifies the degree of the polynomial to fit. You can adjust this to fit a different degree polynomial as needed.