 degree to which two nominal or ordinal variables are related,
or the level of their association. This differs from correlation, although
many often mistakenly consider them equivalent. Correlation measures in
what way two variables are related, whereas, association measures how
related the variables are. As such, association does not subsume
independent variables, and is rather a test of independence. A value of
1.0 indicates perfect association, and 0.0 means the variables have no
association.

Both the Cramer's V and Tschuprow's T are extensions of the phi
coefficient.  Moreover, due to the close relationship between the
Cramer's V and Tschuprow's T the returned values can often be similar
or even equivalent.  They are likely to diverge more as the array shape
diverges from a 2x2.

References
----------
.. [1] "Tschuprow's T",
       https://en.wikipedia.org/wiki/Tschuprow's_T
.. [2] Tschuprow, A. A. (1939)
       Principles of the Mathematical Theory of Correlation;
       translated by M. Kantorowitsch. W. Hodge & Co.
.. [3] "Cramer's V", https://en.wikipedia.org/wiki/Cramer's_V
.. [4] "Nominal Association: Phi and Cramer's V",
       http://www.people.vcu.edu/~pdattalo/702SuppRead/MeasAssoc/NominalAssoc.html
.. [5] Gingrich, Paul, "Association Between Variables",
       http://uregina.ca/~gingrich/ch11a.pdf

Examples
--------
An example with a 4x2 contingency table:

>>> import numpy as np
>>> from scipy.stats.contingency import association
>>> obs4x2 = np.array([[100, 150], [203, 322], [420, 700], [320, 210]])

Pearson's contingency coefficient

>>> association(obs4x2, method="pearson")
0.18303298140595667

Cramer's V

>>> association(obs4x2, method="cramer")
0.18617813077483678

Tschuprow's T

>>> association(obs4x2, method="tschuprow")
0.14146478765062995
z$`observed` must be an integer array.rD