statsmodels.stats.contingency_tables.SquareTable

class statsmodels.stats.contingency_tables.SquareTable(table, shift_zeros=True)

Methods for analyzing a square contingency table.

Parameters:

table : array-like

A square contingency table, or DataFrame that is converted to a square form.

shift_zeros : boolean

If True and any cell count is zero, add 0.5 to all values in the table.

These methods should only be used when the rows and columns of the :

table have the same categories. If `table` is provided as a :

Pandas DataFrame, the row and column indices will be extended to :

create a square table. Otherwise the table should be provided in :

a square form, with the (implicit) row and column categories :

appearing in the same order. :

Methods

chi2_contribs()
cumulative_log_oddsratios()
cumulative_oddsratios()
fittedvalues()
from_data(data[, shift_zeros])	Construct a Table object from data.
homogeneity([method])	Compare row and column marginal distributions.
independence_probabilities()
local_log_oddsratios()
local_oddsratios()
marginal_probabilities()
resid_pearson()
standardized_resids()
summary([alpha, float_format])	Produce a summary of the analysis.
symmetry([method])	Test for symmetry of a joint distribution.
test_nominal_association()	Assess independence for nominal factors.
test_ordinal_association([row_scores, ...])	Assess independence between two ordinal variables.

Methods

chi2_contribs()
cumulative_log_oddsratios()
cumulative_oddsratios()
fittedvalues()
from_data(data[, shift_zeros])	Construct a Table object from data.
homogeneity([method])	Compare row and column marginal distributions.
independence_probabilities()
local_log_oddsratios()
local_oddsratios()
marginal_probabilities()
resid_pearson()
standardized_resids()
summary([alpha, float_format])	Produce a summary of the analysis.
symmetry([method])	Test for symmetry of a joint distribution.
test_nominal_association()	Assess independence for nominal factors.
test_ordinal_association([row_scores, ...])	Assess independence between two ordinal variables.