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variance = row[POINT_ESTIMATE] * (1 - row[POINT_ESTIMATE]) if variance < 0: raise ValueError('Computed variance is negative. ' 'Please check your inputs.') return variance
row[CI_LOWER], row[CI_UPPER] = proportion_confint( count=row[self._numerator], nobs=row[self._denominator], alpha=1-self._interval_size, ) return row
_, p_value, _ = ( proportions_chisquare(count=[row[self._numerator + SFX1], row[self._numerator + SFX2]], nobs=[row[self._denominator + SFX1], row[self._denominator + SFX2]]) ) return p_value
return confint_proportions_2indep( count1=row[self._numerator + SFX2], nobs1=row[self._denominator + SFX2], count2=row[self._numerator + SFX1], nobs2=row[self._denominator + SFX1], alpha=row[alpha_column], compare='diff', method='wald' )
df: DataFrame, mde: float, alpha: float) -> DataFrame: s1, s2 = df[self._numerator + SFX1], df[self._numerator + SFX2] n1, n2 = df[self._denominator + SFX1], df[self._denominator + SFX2]
pooled_prop = (s1 + s2) / (n1 + n2) var_pooled = pooled_prop * (1 - pooled_prop)
return power_calculation(mde, var_pooled, alpha, n1, n2) |