One of the assumptions made by least squares method is that the residuals are independent and normally distributed with same parameters (or, in case of weighted regression, the standard deviation of the residual is known for every point). If this is the case, the parameters that minimize the sum of squared residuals are the maximum likelihood estimation of the true parameter values.
The problem is, your data doesn't seem to adhere well to your formula. Have you tried plotting your HF1 - ((m/HF6) + 1) against HF6 (i.e. the residuals themselves)? With large residual values (outliers?), the loss function (i.e. sum of squared residuals) is disturbed and doesn't reflect the values you would expect to get otherwise. Try computing sum((HF1 - ((m/HF6) + 1))^2) for different values of m and see if changing m makes any difference. Try looking up "robust regression" (e.g. minimize sum of absolute residuals instead of squared residuals; a unique solution is not guaranteed, but it's be less disturbed by outliers). -- Best regards, Ivan ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
