Reduced chi square for few data points

[Malamala]

Hello! I need to make a straight line fit to 8 points, with errors on them. The data is like this $x = [1,2,3,4,5,6,7,8]$, $y=[377.488 691.191 , 1030.319, 1428.801, 1753.884, 2113.065 , 2398.642, 2797.664]$, $y_{err}=[97.145, 131.452, 160.492, 188.997, 209.397, 229.840, 244.879, 264.464]$. The problem is that the errorbars (which are basically Poisson errors) are a lot bigger than the spread of the data. Hence I end up with a reduced chi-squared of 0.02. Is there anything I can do? The values of the fits are reasonable (given theoretical arguments), but I am not sure what to make out of this small reduced chi-square.

 

[BvU]

Did you make a plot ?
Tell us how the data were obtained and what they represent. Especially the $y_{err}$.
And how can you obtain such unbelievably accurate estimates of $y_{err}$. Billions of observations, or just mindless copying calculation results ?
Are you aware of the role of systematic errors ?

How are the other threads going ? Long forgotten ?

I'd like to know if you understood the help given in earlier threads, or just lost interest and never reacted any more. I don't mind repeating things, but it should have a reasonable purpose.