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.

See also

statsmodels.graphics.mosaicplot.mosaic
scipy.stats.chi2_contingency

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.
`test_nominal_association`()	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.