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Starting with the "Math Is Fun" level:
https://www.mathsisfun.com/data/chi-square-test.html
Continuing with the choices offered in the Indian journal of Ophthalmology:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3116565/
Another Indian source for considering suitable tests:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2996580/
Finally an Italian examination of how to validate competing models
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2996580/
They all seem to derogate categorical tests like Chi squared for
graph-like data sources of 80 points.
Brian W
(not sure why American sources do not make a better
showing in my search?)
On 4/12/2019 10:58 AM, Paul Nord wrote:
"Ask n statisticians, get C(n,k) opinions."translation
- me, just now
Suppose I have some physics data binned into 80 bins. And I have two
models which propose to fit some characteristic of interest and a
non-linear background. One is a simple polynomial. The other is a more
complicated. I can calculate a reduced chi square for each data fit.
What's the best way to compare the two models?
I have a dim memory of this lecture. Does someone have a good
of said lecture into terms a physicist can understand?
Paul
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