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Hi,
I have following bivariate data.
(y) 800 1000 1200

(x) 
1150 1380.4 1500.2 1614.5

1200 1377.7 1499.0 1613.6

1250 1375.2 1497.1 1612.6
The correct solution for this in terms of bivariate equation (a+bx+cy+dx^2+ey^2+fxy)
914.033  0.0205000x + 0.645500y  0.000040000x^2  0.000077500y^2 + 0.000082500xy.
But when i use matlab c=inv(A) * b, i get coefficient which produces more errors. Following are the coefficient produced by matlab
a = 8.12E+03
1.52E+01
4.61E01
6.46E03
8.56E05
2.50E04
What i am looking for is high accuracy in terms of 0.5 error or less than that. Is there any other way/tool i can calculate the coefficients with very high accuracy? pls suggest.
matlab code:
a=[9 10800 9000 12975000 9240000 10800000
10800 12975000 10800000 15606000000 11088000000 12975000000
9000 10800000 9240000 12975000000 9720000000 11088000000
12975000 15606000000 12975000000 18792000000000 13321000000000 15606000000000
9240000 11088000000 9720000000 13321000000000 10450000000000 11664000000000
10800000 12975000000 11088000000 15606000000000 11664000000000 13321000000000];
b = [13470.3
16163800
13611800
19418500000
14112200000
16333700000];
ans = inv(a)*b
Last edited by elamkumaran (20131111 03:27:06)
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Hi;
I can not see your bivariate xy data, please format it a little better.
In mathematics, you don't understand things. You just get used to them.
If it ain't broke, fix it until it is.
No great discovery was ever made without a bold guess.
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Hi,
I hope you can see now..pls confirm
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Hi;
That is the correct least squares fit.
Now from there you have an overdetermined system of linear equations. Since a is a 9 x 6 matrix you can not invert it.
We start with these 9 equations:
When we substitute x and y for each point we get:
Now the formula to least square an overdetermined system is
The final bivariate polynomial is
which is the same as above.
In mathematics, you don't understand things. You just get used to them.
If it ain't broke, fix it until it is.
No great discovery was ever made without a bold guess.
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