Analyses that can be performed on a two-way contingency table.
Parameters: | table : array-like
shift_zeros : boolean
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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
Attributes
marginal_probabilities() | |
independence_probabilities() | |
fittedvalues() | |
resid_pearson() | |
standardized_resids() | |
chi2_contribs() | |
local_oddsratios() | |
cumulative_log_oddsratios() | |
cumulative_oddsratios() |
table_orig | array-like | The original table is cached as table_orig. |
local_logodds_ratios | ndarray | The local log odds ratios are calculated for each 2x2 subtable formed from adjacent rows and columns. |
Methods
chi2_contribs() | |
cumulative_log_oddsratios() | |
cumulative_oddsratios() | |
fittedvalues() | |
from_data(data[, shift_zeros]) | Construct a Table object from data. |
independence_probabilities() | |
local_log_oddsratios() | |
local_oddsratios() | |
marginal_probabilities() | |
resid_pearson() | |
standardized_resids() | |
test_nominal_association() | Assess independence for nominal factors. |
test_ordinal_association([row_scores, ...]) | Assess independence between two ordinal variables. |
Methods
chi2_contribs() | |
cumulative_log_oddsratios() | |
cumulative_oddsratios() | |
fittedvalues() | |
from_data(data[, shift_zeros]) | Construct a Table object from data. |
independence_probabilities() | |
local_log_oddsratios() | |
local_oddsratios() | |
marginal_probabilities() | |
resid_pearson() | |
standardized_resids() | |
test_nominal_association() | Assess independence for nominal factors. |
test_ordinal_association([row_scores, ...]) | Assess independence between two ordinal variables. |