Minimum chi squared estimation

[indie452]

hi
i have some data (star counts) and i have a model and i want to perform min chi squared

so if i call my data di, and my model mi with std dev = [itex]\sigma[/itex]i = 1

then [itex]\chi^2 = \sum \frac{(di - mi)^2}{\sigma i^2}[/itex]

no my model is this mi = bi - Fo where bi is the background which has been assumed to be 5, and Fo is some constant flux. from this i am thus assuming that the data is ecpected to be a flat line.

Now i want to determine the min ch squared estimate for Fo, but i am not sure how.
I have gotten this far:
[itex]\frac{d\chi ^2}{dFo} = -2\sum \frac{(di - bi - Fo)}{\sigma i^2}[/itex] = 0

any help is appreciated thanks

 

[Valkarie]

!To find the minimum Chi squared estimate for Fo, you need to solve the equation: \frac{d\chi ^2}{dFo} = -2\sum \frac{(di - bi - Fo)}{\sigma i^2} = 0 for Fo. Rearranging the equation gives Fo = \sum \frac{di - bi}{\sigma i^2} which is the minimum chi squared estimate for Fo.