# How can I modify this code so it gives the value 0 for c. It should only provide ax^2+bx

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Ahmed on 4 Dec 2020
function [coefficients, MSE] = pnnnfit(x, y, order)
coefficients = polyfit(x, y, order)
y_fitted = polyval(coefficients, x)
squaredError = (y - y_fitted) .^ 2
MSE = sum(squaredError)
end
%x=[0 .5 1 1.5 2 2.5 3 3.5 4 4.5 5];
% y=[0 -5.27 -8.10 -8.46 -6.38 -1.84 5.15 14.59 26.48 40.83 57.63];
ans =
4.9069 -13.0140 0.0265
Walter Roberson on 4 Dec 2020
If you mean that a^2+b*x should exactly equal y, then that is not the case.
The best degree 2 fit is 61067/12450 * x^2 - 17802227/1369500*x + 10357/136950000

%x=[0 .5 1 1.5 2 2.5 3 3.5 4 4.5 5];
%y=[0 -5.27 -8.10 -8.46 -6.38 -1.84 5.15 14.59 26.48 40.83 57.63];
function [coefficients] = pnnnfit(x, y)
coefficients=[sum(x.^4) sum(x.^3) ; sum(x.^3) sum(x.^2)]\[sum(y.*x.^2) sum(y.*x)]';
% a = coefficients(1)
% b = coefficients(2)
% you need to add more code to find MSE but it is simple
plot(x,y,x,coefficients(1)*x.^2+coefficients(2)*x)
end
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Yes, you are right. I used the actual mathematical equations for the least squares method. Matlab has a simplified '\' operator for solving equations and the least square method