least square matlab in matlab

The polyfit function in MATLAB can be used to perform least squares regression. It can fit a polynomial model of degree n to the given data using the n+1 data points.

Here's the MATLAB code for performing a least squares regression for a linear model of the form y = mx + b:

main.m
% Input data
x = [1, 2, 3, 4, 5];
y = [0.5, 1.5, 2.8, 4.1, 5.2];

% Perform least squares regression
p = polyfit(x, y, 1);

% Extract coefficients of the fitted line
m = p(1);
b = p(2);

% Plot the data and fitted line
scatter(x, y)
hold on
plot(x, m*x + b)
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In the above code, x and y are the input data points. The polyfit function is used to fit a linear polynomial to the data (1 indicates that we want a linear model). The coefficients of the fitted line are then extracted from the result of polyfit. Finally, the data and fitted line are plotted using the scatter and plot functions, respectively.