statsmodels.stats.oneway.anova_generic¶

statsmodels.stats.oneway.anova_generic(means, variances, nobs, use_var='unequal', welch_correction=True, info=None)[source]¶

Oneway Anova based on summary statistics

Parameters:

meansarray_like: Mean of samples to be compared
variancesfloat or array_like: Residual (within) variance of each sample or pooled. If variances is scalar, then it is interpreted as pooled variance that is the same for all samples, use_var will be ignored. Otherwise, the variances are used depending on the use_var keyword.
nobsint or array_like: Number of observations for the samples. If nobs is scalar, then it is assumed that all samples have the same number nobs of observation, i.e. a balanced sample case. Otherwise, statistics will be weighted corresponding to nobs. Only relative sizes are relevant, any proportional change to nobs does not change the effect size.
use_var{“unequal”, “equal”, “bf”}: If use_var is “unequal”, then the variances can differ across samples and the effect size for Welch anova will be computed.
welch_correctionbool: If this is false, then the Welch correction to the test statistic is not included. This allows the computation of an effect size measure that corresponds more closely to Cohen’s f.
infonot used yet

Returns: