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The question is in the title: In both cases I use $R^2$ and chi-square to test if a fit/model is good enough. Up to now I only know that $R^2$ is used for models (?) and chi-square for fits/functions (?). Is this true? And how exactly do they differ?

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  • $\begingroup$ The answer to this would almost amount to a complete introductory statistics course. Can you narrow it down a bit to a specific scientific analysis where you are in doubt what to do? $\endgroup$
    – mdewey
    Aug 1, 2017 at 12:52
  • $\begingroup$ I'm just learning these two terms because I always stumble over them. And it confuses me. Also what's the difference between max likelihood and chi-square.. for me they are all doing the same and it's a bit hard to separate them (for me). $\endgroup$
    – Ben
    Aug 1, 2017 at 12:56

2 Answers 2

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Found this after a quick Google search:

$R^2$ is used to quantify the amount of variability in the data that is explained by your model. It's useful for comparing the fits of different models.

The chi-square goodness of fit test is used to test if your data follows a particular distribution. It's more useful for testing model assumptions rather than comparing models.

Sounds like the chi-square is more useful if you have a function you are trying to test (or a model you are trying to fit to your data) as opposed to the $R^2$, which tells you how much variability there is in your data, and therefore how much the best model fits.

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  • $\begingroup$ Thanks! So that's my assumption :) But thanks anyway! Of interested is now how/why they are applied to different cases respectively in which way they work differently. $\endgroup$
    – Ben
    Aug 1, 2017 at 13:07
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The chi-square provides a per-feature measurement of dependency with the target. This is useful at the feature-selection stage, for a classification model. We'd like to weed out the low-dependent features. (scikit-learn guide for additional such measurements for classification and regression models).

$R^2$ provides a model-level measurement of the target's variance explained. This is useful at the model-evaluation stage. (scikit-learn guide for $R^2$)

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