statanalysis.hyp_vali_md package
Submodules
les test de valisation (hypothese avant de lancer un autre test) qui dependant de test que j’ai écrits moi-même Les mettre dans utils prut créer un import circulaire
- statanalysis.hyp_vali_md.hypothesis_validator.check_coefficients_non_zero(list_coeffs: list, list_coeff_std: list, nb_obs: int, debug=False, alpha=None)
compute non zero tests for each corfficien - test
- for ech coefficient
H0: coeff==0
H1: coeff!=0
if the test passed (H0 is rejected), the coefficient is away from 0, return = True
- Parameters:
list_coeffs (list) – lists of values
list_coeff_std (list) – list of std; the two lists should have the same lenght
- Returns:
- HypothesisValidationData(pass_non_zero_test_bool,pass_non_zero_test)
testPassed (bool)
obj (list) list of boolean (For each value, True if H0 is reected)
- statanalysis.hyp_vali_md.hypothesis_validator.check_equal_mean(*samples, alpha=None)
check if mean if the same accross samples
- Hypothesis
H0: mean1 = mean2 = mean3 = …. H1: one is different
- Hypothesis
The samples are independent.
Each sample is from a normally distributed population.
The population standard deviations of the groups are all equal. This property is known as homoscedasticity.
- Parameters:
*samples (-) –
one or many lists
- Fisher test
The F Distribution is also called the Snedecor’s F, Fisher’s F or the Fisher–Snedecor distribution [1](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.f_oneway.html) [2](https://blog.minitab.com/fr/comprendre-lanalyse-de-la-variance-anova-et-le-test-f)
- Returns:
(float) F p_value: (float)
- Return type:
stat
- statanalysis.hyp_vali_md.hypothesis_validator.check_residuals_centered(residuals: list, alpha=None)
check if a list is centered (if the mean ==0 nuder a significance od 0.05)
- Parameters:
residuals (list) – list or array-like
- Returns:
_description_
- Return type:
_type_