Coverage for /Users/pers/GitHub/confidence/spotify_confidence/analysis/frequentist/confidence_computers/t_test_computer.py : 44%

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STD_ERR, PREFERENCE_TEST, NULL_HYPOTHESIS, DIFFERENCE
variance = ( row[self._numerator_sumsq] / row[self._denominator] - row[POINT_ESTIMATE] ** 2) if variance < 0: raise ValueError('Computed variance is negative. ' 'Please check your inputs.') return variance
row[CI_LOWER], row[CI_UPPER] = _tconfint_generic( mean=row[POINT_ESTIMATE], std_mean=np.sqrt(row[VARIANCE] / row[self._denominator]), dof=row[self._denominator] - 1, alpha=1-self._interval_size, alternative=TWO_SIDED ) return row
v1, v2 = row[VARIANCE + SFX1], row[VARIANCE + SFX2] n1, n2 = row[self._denominator + SFX1], row[self._denominator + SFX2] return ((v1 / n1 + v2 / n2) ** 2 / ((v1 / n1) ** 2 / (n1 - 1) + (v2 / n2) ** 2 / (n2 - 1)))
_, p_value = _tstat_generic(value1=row[POINT_ESTIMATE + SFX2], value2=row[POINT_ESTIMATE + SFX1], std_diff=row[STD_ERR], dof=self._dof(row), alternative=row[PREFERENCE_TEST], diff=row[NULL_HYPOTHESIS]) return p_value
return _tconfint_generic( mean=row[DIFFERENCE], std_mean=row[STD_ERR], dof=self._dof(row), alpha=row[alpha_column], alternative=row[PREFERENCE_TEST])
df: DataFrame, mde: float, alpha: float) -> DataFrame: v1, v2 = df[VARIANCE + SFX1], df[VARIANCE + SFX2] n1, n2 = df[self._denominator + SFX1], df[self._denominator + SFX2]
var_pooled = ((n1 - 1) * v1 + (n2 - 1) * v2) / (n1 + n2 - 2)
return power_calculation(mde, var_pooled, alpha, n1, n2) |