Methods for analyzing a square contingency table.
Parameters: | table : array-like
shift_zeros : boolean
These methods should only be used when the rows and columns of the : table have the same categories. If `table` is provided as a : Pandas DataFrame, the row and column indices will be extended to : create a square table. Otherwise the table should be provided in : a square form, with the (implicit) row and column categories : appearing in the same order. : |
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Methods
chi2_contribs() | |
cumulative_log_oddsratios() | |
cumulative_oddsratios() | |
fittedvalues() | |
from_data(data[, shift_zeros]) | Construct a Table object from data. |
homogeneity([method]) | Compare row and column marginal distributions. |
independence_probabilities() | |
local_log_oddsratios() | |
local_oddsratios() | |
marginal_probabilities() | |
resid_pearson() | |
standardized_resids() | |
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. |
Methods
chi2_contribs() | |
cumulative_log_oddsratios() | |
cumulative_oddsratios() | |
fittedvalues() | |
from_data(data[, shift_zeros]) | Construct a Table object from data. |
homogeneity([method]) | Compare row and column marginal distributions. |
independence_probabilities() | |
local_log_oddsratios() | |
local_oddsratios() | |
marginal_probabilities() | |
resid_pearson() | |
standardized_resids() | |
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. |