unipy.stats.hypo_test module

Statistical Hypothesis Tests.

unipy.stats.hypo_test.f_test(a, b, scale=1, alternative='two-sided', conf_level=0.95, *args, **kwargs)

F-Test.

unipy.stats.hypo_test.f_test_formula(a, b, scale=1, alternative='two-sided', conf_level=0.95, *args, **kwargs)

F-Test by formula.

unipy.stats.hypo_test.anova_test(formula, data=None, typ=1)

ANOVA Test.

unipy.stats.hypo_test.anova_test_formula(formula, data=None, typ=1)

ANOVA Test by formula.

unipy.stats.hypo_test.chisq_test(data, x=None, y=None, correction=None, lambda_=None, margin=True, print_ok=True)

Chi-square Test.

lambda_ gives the power in the Cressie-Read power divergence statistic. The default is 1. For convenience, lambda_ may be assigned one of the following strings, in which case the corresponding numerical value is used:

Parameters

• data (pandas.DataFrame) –

• x (str (default: None)) –

• y (str (default: None)) –

• correction ((default: None)) –

• lambda_ (lambda (default: None)) –

• margin (Boolean (default: True)) –

• print_ok (Boolean (default: True)) –

unipy.stats.hypo_test.fisher_test(data, x=None, y=None, alternative='two-sided', margin=True, print_ok=True)

Fisher’s Exact Test.