ttest_ind — SciPy v1.14.0 Manual
ttest_ind# scipy.stats.ttest_ind(a, b, axis=0, equal_var=True, nan_policy='propagate', permutations=None, random_state=None, alternative='two-sided', trim=0, *, keepdims=False)[source]# Calculate the T-test for the means of two independent samples of scores. This is a test for the null hypothesis that 2 independent samples have identical average (expected) values. This test assumes that the popula
scipy.stats.zscore — SciPy v0.19.0 Reference Guide
scipy.stats.zscore¶ scipy.stats.zscore(a, axis=0, ddof=0)[source]¶ Calculates the z score of each value in the sample, relative to the sample mean and standard deviation. Parameters:
scipy.stats.bernoulli — SciPy v0.19.0 Reference Guide
scipy.stats.bernoulli¶ scipy.stats.bernoulli = <scipy.stats._discrete_distns.bernoulli_gen object at 0x2b2318c12cd0>[source]¶ A Bernoulli discrete random variable. As an instance of the rv_discrete class, bernoulli object inherits from it a collection of generic methods (see below for the full list), and completes them with details specific for this particular distribution. Notes The probability m
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scipy.stats.kendalltau — SciPy v1.13.0 Manual
scipy.stats.spearmanr — SciPy v1.13.0 Manual
scipy.stats.pearsonr — SciPy v1.13.0 Manual
scipy.stats.zscore — SciPy v1.13.0 Manual
scipy.stats.fisher_exact — SciPy v0.14.0 Reference Guide
