# statsmodels.stats.contingency_tables.SquareTable¶

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

Methods for analyzing a square contingency table.

Parameters
tablearray_like

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

shift_zerosbool

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, inserting zeros where a row or column is
missing. Otherwise the table should be provided in a square form,
with the (implicit) row and column categories appearing in the
same order.

Methods

 from_data(data[, shift_zeros]) Construct a Table object from data. homogeneity([method]) Compare row and column marginal distributions. summary([alpha, float_format]) Produce a summary of the analysis. symmetry([method]) Test for symmetry of a joint distribution. Assess independence for nominal factors. test_ordinal_association([row_scores, …]) Assess independence between two ordinal variables.

Properties

 chi2_contribs Returns the contributions to the chi^2 statistic for independence. cumulative_log_oddsratios Returns cumulative log odds ratios. cumulative_oddsratios Returns the cumulative odds ratios for a contingency table. fittedvalues Returns fitted cell counts under independence. independence_probabilities Returns fitted joint probabilities under independence. local_log_oddsratios Returns local log odds ratios. local_oddsratios Returns local odds ratios. marginal_probabilities Estimate marginal probability distributions for the rows and columns. resid_pearson Returns Pearson residuals. standardized_resids Returns standardized residuals under independence.