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Least-squares fitting a second-order polynomial to data
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This screen capture video is from my course "Applications of matrix computations," lecture given on April 11, 2018 at University of Helsinki, Finland.
Least-squares fitting a second-order polynomial to data.
We consider the problem of fitting a parabola to a set of measurement points. Our starting point is seven data points (real numbers) m1, m2, ..., m7, measured at times t1=1, t2=2, ..., t7=7 seconds. Here they are not really measured; we just gave some values that came to mind.
We assume an ideal quadratic model of the form m=at^2+bt+c, where a, b and c are unknown numbers to be solved.
The problem is written as a 7x3 matrix equation and solved in the sense of least squares using pseudoinverse.
Least-squares fitting a second-order polynomial to data.
We consider the problem of fitting a parabola to a set of measurement points. Our starting point is seven data points (real numbers) m1, m2, ..., m7, measured at times t1=1, t2=2, ..., t7=7 seconds. Here they are not really measured; we just gave some values that came to mind.
We assume an ideal quadratic model of the form m=at^2+bt+c, where a, b and c are unknown numbers to be solved.
The problem is written as a 7x3 matrix equation and solved in the sense of least squares using pseudoinverse.
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