Timeline for Reduced chi square value

4 events

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Jan 13, 2023 at 12:55	comment	added	David Hammen		That is correct. Sometimes it's better to take a stepwise approach of looking at each model parameter individually and adding the one that results in the best improvement to the model, repeating until there are no modeling parameters left or the improvement is negligible. An alternative is to start with using all modeling parameters on the first round and then eliminating modeling parameters one by one based on some measure of statistical (in)significance, stopping the trimming when removing any one of the remaining parameters would trim too much (from a statistically significant perspective).
Jan 13, 2023 at 12:42	comment	added	ProfRob		@DavidHammen it can be more complicated. For example the true function could be odd or even in which case adding even or odd polynomial terms won't change the chi-squared and the reduced chi-squared will increase. But adding a further term could then see a significant decrease.
Jan 13, 2023 at 11:59	comment	added	David Hammen		Another way to put the latter part of the final sentence is that the point at which the reduced chi-squared does not decrease significantly from the previous iteration with one fewer degree of freedom is the point at which one might well be overfitting.
Jan 12, 2023 at 20:24	history	answered	ProfRob	CC BY-SA 4.0