statsmodels.stats.oneway.anova_oneway¶

statsmodels.stats.oneway.anova_oneway(data, groups=None, use_var='unequal', welch_correction=True, trim_frac=0)[source]¶

Oneway Anova

This implements standard anova, Welch and Brown-Forsythe, and trimmed (Yuen) variants of those.

Parameters:¶

datatuple of array_like or DataFrame or Series

Data for k independent samples, with k >= 2. The data can be provided as a tuple or list of arrays or in long format with outcome observations in data and group membership in groups.

groupsndarray or Series

If data is in long format, then groups is needed as indicator to which group or sample and observations belongs.

use_var{“unequal”, “equal” or “bf”}

use_var specified how to treat heteroscedasticity, unequal variance, across samples. Three approaches are available

“unequal”Variances are not assumed to be equal across samples.

Heteroscedasticity is taken into account with Welch Anova and Satterthwaite-Welch degrees of freedom. This is the default.

“equal”Variances are assumed to be equal across samples.

This is the standard Anova.

“bf”Variances are not assumed to be equal across samples.

The method is Browne-Forsythe (1971) for testing equality of means with the corrected degrees of freedom by Merothra. The original BF degrees of freedom are available as additional attributes in the results instance, df_denom2 and p_value2.

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.

trim_fracfloat in [0, 0.5)

Optional trimming for Anova with trimmed mean and winsorized variances. With the default trim_frac equal to zero, the oneway Anova statistics are computed without trimming. If trim_frac is larger than zero, then the largest and smallest observations in each sample are trimmed. The number of trimmed observations is the fraction of number of observations in the sample truncated to the next lower integer. trim_frac has to be smaller than 0.5, however, if the fraction is so large that there are not enough observations left over, then nan will be returned.

Returns:¶

resresults instance

The returned HolderTuple instance has the following main attributes and some additional information in other attributes.

statisticfloat

Test statistic for k-sample mean comparison which is approximately F-distributed.

pvaluefloat

If use_var="bf", then the p-value is based on corrected degrees of freedom following Mehrotra 1997.

pvalue2float

This is the p-value based on degrees of freedom as in Brown-Forsythe 1974 and is only available if use_var="bf".

df = (df_denom, df_num)tuple of floats

Degreeds of freedom for the F-distribution depend on use_var. If use_var="bf", then df_denom is for Mehrotra p-values df_denom2 is available for Brown-Forsythe 1974 p-values. df_num is the same numerator degrees of freedom for both p-values.

See also

anova_generic

Notes

Welch’s anova is correctly sized (not liberal or conservative) in smaller samples if the distribution of the samples is not very far away from the normal distribution. The test can become liberal if the data is strongly skewed. Welch’s Anova can also be correctly sized for discrete distributions with finite support, like Lickert scale data. The trimmed version is robust to many non-normal distributions, it stays correctly sized in many cases, and is more powerful in some cases with skewness or heavy tails.

Trimming is currently based on the integer part of nobs * trim_frac. The default might change to including fractional observations as in the original articles by Yuen.

References

Brown, Morton B., and Alan B. Forsythe. 1974. “The Small Sample Behavior of Some Statistics Which Test the Equality of Several Means.” Technometrics 16 (1) (February 1): 129–132. doi:10.2307/1267501.

Mehrotra, Devan V. 1997. “Improving the Brown-Forsythe Solution to the Generalized Behrens-Fisher Problem.” Communications in Statistics - Simulation and Computation 26 (3): 1139–1145. doi:10.1080/03610919708813431.