The best values of the coefficients are the ones that minimize the value of Chi-square. We want to find values for the coefficients such that the function matches the raw data as well as possible. In curve fitting we have raw data and a function with unknown coefficients. Some people try to use curve fitting to find which of thousands of functions fit their data. The curve fit finds the specific coefficients (parameters) which make that function match your data as closely as possible. We assume that you have theoretical reasons for picking a function of a certain form. The idea of curve fitting is to find a mathematical model that fits your data. Packages built on Igor's basic curve fitting capability add functionality: Programmer support for simultaneous fits using multiple processors.User-defined fits take advantage of multiple processors.Fully programmable for repetitive or unusual curve fitting tasks.Follow fit progress with automatic graph updates during iterative fits.For simple fits to built-in functions, fit with a single menu selection. ![]()
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