# statsmodels.stats.contingency_tables.Table¶

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

A two-way contingency table.

Parameters
tablearray_like

A contingency table.

shift_zerosbool

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

Notes

The inference procedures used here are all based on a sampling model in which the units are independent and identically distributed, with each unit being classified with respect to two categorical variables.

References

Definitions of residuals:

https://onlinecourses.science.psu.edu/stat504/node/86

Attributes
table_origarray_like

The original table is cached as table_orig.

Methods

 from_data(data[, shift_zeros]) Construct a Table object from data. Assess independence for nominal factors. test_ordinal_association([row_scores, …]) Assess independence between two ordinal variables.

Methods

 from_data(data[, shift_zeros]) Construct a Table object from data. 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.