Is the Least Squares Fit always the accepted Fit in an experiment.? Suppose you have $N$ data points and a function $f$ with some parameters. There is a one Least Square sum, it may be obtained for more than one set of parameters but we can find that most optimal fit. Is it true that sometimes that Least Square fit is discarded because the expected values for the parameters are assumed to be different?
Why is it necessary to give starting values to the algorithms? Why can't the computer find those parameters on its own?
