Hello , I am dealing with linear least square regression
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.I successfully found the coefficients for the quadratic function as follows.
function [a2,a1,a0] = QuadraticRegression (x,y)
x=[10 15 25 40 50 55];
y=[94 116 147 183 215 220];
n = length (x)
sx = sum(x)
sy = sum(y)
sx2 = sum(x.^2)
sx3 = sum(x.^3)
sx4 = sum(x.^4)
sxy = sum(x.*y)
A = [ n sx sx2; sx sx2 sx3;sx2 sx3 sx4]
b = [sy;sxy;sx2y]
a2 = w(3)
a1 = w(2)
a0 = w(1)
func(y) = -0.0170 x.^2 + 3.8870 x + 59.0062
now i'm suppose to fit the above data into a power function as follows : y = Q (x.^M) Where Q and M are coefficients.
I went through the book https://books.google.com/books/about/Numerical_Methods_for_Engineers_and_Scie.html?id=gMrMBQAAQBAJ&printsec=frontcover&source=kp_read_button#v=onepage&q&f=false
and went through Youtube and google before i posted my question here. I see no method mentions in the any of the resources. Please give me some clues. Thanks
Torsten on 16 Mar 2018
Edited: Torsten on 16 Mar 2018
y = Q*x^M => log(y) = log(Q) + M*log(x)
Thus take "log" of your x- and y-data and fit them by a linear function
y~ = a+b*x~
y~=log(y) and x~=log(x).
Once you have determined a and b, Q=exp(a) and M=b.
Another possibility is to use "lsqcurevfit" directly on the nonlinear function y = Q*x^M.
This will give slightly different fit parameters for Q and M because taking the log of x and y and making a linear fit as suggested above somehow distorts the data.