Hello, Let me get right to my problem. I have an experimental distribution and a single-parameter theoretical distribution. I want to find the value for the best fit theoretical distribution that agrees with my experimental data for the bulk of the distributions (the tails of my distributions differ substantially). I isolate an equal portion of both distributions and calculate the sum of the squares of the differences between the two distributions for this region. i.e. least squares approach. R2=Ʃ [ expi - theoi(x) ]2 I do this for several values that I have chosen for the single-parameter theoretical distribution and obtain a unique parameter value (which I call x=ζ) which results in the minimization of the sum of the squares (exactly what I want). Every other parameter gives a larger value for this sum of squares. I do not know if this is necessary but I can plot all parameters that I tested vs the R2 too. This gives points that combine to form a parabola, ax2+bx+c=R2. I can take the derivative of the parabola curve to obtain the minimum of the parabola which again occurs at ζ. I have attached a pdf of this. My question is, how do I find a confidence interval for this? I am looking for [itex]\sigma[/itex] in ζ ± [itex]\sigma[/itex]. Do I use the formula for the parabola to find this? Do I use R2? I have looked through some books and online and been unsuccessful on how to find this [itex]\sigma[/itex] value. Any help is appreciated. A reference book would be of great help too. Thanks!