This shows you the differences between two versions of the page.
Both sides previous revision Previous revision Next revision | Previous revision | ||
fit_formulas [Wed Jun 22 21:39:11 2016] Alexander |
fit_formulas [Tue May 30 16:28:13 2017] (current) Alexander |
||
---|---|---|---|
Line 15: | Line 15: | ||
* //f//(//x, β< | * //f//(//x, β< | ||
* //p// is the number of fit parameters // | * //p// is the number of fit parameters // | ||
- | * // | + | * // |
An initial guess for the parameters has to be provided to start minimization. Calculation of the new guess of parameters on each fit iteration is based on the fit function partial derivatives for current values of fit parameters for each //x// value: | An initial guess for the parameters has to be provided to start minimization. Calculation of the new guess of parameters on each fit iteration is based on the fit function partial derivatives for current values of fit parameters for each //x// value: | ||
Line 25: | Line 25: | ||
===== Weighting of Data Points Using Y Errors ===== | ===== Weighting of Data Points Using Y Errors ===== | ||
MagicPlot can use weighting of //y// values based on y errors // | MagicPlot can use weighting of //y// values based on y errors // | ||
- | * If standard //y// errors are **not** specified: all // | ||
- | * If standard //y// errors // | ||
- | <m>w_i=C 1/{{s_i}^2}</m> | + | * If standard //y// errors //s<sub>i</ |
- | + | * Otherwise: all //w<sub>i</sub>// = 1. | |
- | here //C// is normalizing coefficient | + | |
- | + | ||
- | <m>C=N sum{i=1}{N}{{s_i}^2}</m> | + | |
In '' | In '' |