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Statistical checks for regression, covariance, etc?

asked 2010-08-29 19:23:54 +0200

ccanonc gravatar image

updated 2015-01-14 10:55:55 +0200

FrédéricC gravatar image

Suppose I have some input vector(s) of scientific data. Assuming I at least know which ones are completely independent, and have some other(s) of which dependence is uncertain; what are some good sage functions to throw at the vector(s) to check for basic statistical parity/significance?

I know this question is very broad, so it's a basic howto rhetorical question. A continuous or discrete first order relationship is a fine intro; though more complex scenarios tend to be interesting.

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answered 2010-08-30 04:54:21 +0200

vdelecroix gravatar image


Depending on what you mean by "scientific data" the answer would be different. If they are in a finite set say {True, False} then you can make a chi^2 test. If they are infinite but can be ordinate then you can perform a Mann-Whitney (or Wilcoxon or Wilcoxon-Mann-Whitney) test...

In the two preceding cases you should use R (which is included in Sage) which is a toolbox for statistics. More precisely you can look at

sage: r.chisq_test?
sage: r.wilcox_test?

(the r. means that you will use R)

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Asked: 2010-08-29 19:23:54 +0200

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Last updated: Aug 30 '10