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

Methods for analyzing a square contingency table.

  • 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) –


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.