# statsmodels.stats.contingency_tables.SquareTable¶

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

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. methods should only be used when the rows and columns of the (These) – have the same categories. If table is provided as a (table) – DataFrame, the row and column indices will be extended to (Pandas) – a square table, inserting zeros where a row or column is (create) – Otherwise the table should be provided in a square form, (missing.) – the (implicit) row and column categories appearing in the (with) – order. (same) –

Methods

 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. from_data(data[, shift_zeros]) Construct a Table object from data. homogeneity([method]) Compare row and column marginal distributions. 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. 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.