\[\]To obtain \(r\) and \(K\) that minimizes \(\,J\), you just take derivative of \(\,J\) with respect to \(r\) and \(K\) and then set equal to \(0\). This technique is called minimizing the mean square error (MSE) or chi-squared (\(\;\chi^2\)) minimization.\[\]
\[
\begin{align}
\frac{\partial J}{\partial r}&=0\tag1\\
\frac{\partial J}{\partial K}&=0.\tag2
\end{align}
\]
\[\]Next, plug in data in the table and solve those two non-linear equations. You can use either Newton-Raphson, Steffenson, or secant method. Of course you should use computer program like Matlab, SAS, R, etc to help you solve \(\,(1)\) and \(\,(2)\).

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TopNewest\[\]To obtain \(r\) and \(K\) that minimizes \(\,J\), you just take derivative of \(\,J\) with respect to \(r\) and \(K\) and then set equal to \(0\). This technique is called minimizing the mean square error (

MSE) or chi-squared (\(\;\chi^2\)) minimization.\[\] \[ \begin{align} \frac{\partial J}{\partial r}&=0\tag1\\ \frac{\partial J}{\partial K}&=0.\tag2 \end{align} \] \[\]Next, plug in data in the table and solve those two non-linear equations. You can use either Newton-Raphson, Steffenson, or secant method. Of course you should use computer program like Matlab, SAS, R, etc to help you solve \(\,(1)\) and \(\,(2)\).Log in to reply