# Statistical functions for masked arrays (scipy.stats.mstats)¶

This module contains a large number of statistical functions that can be used with masked arrays.

Most of these functions are similar to those in scipy.stats but might have small differences in the API or in the algorithm used. Since this is a relatively new package, some API changes are still possible.

 argstoarray(*args) Constructs a 2D array from a group of sequences. chisquare(f_obs[, f_exp, ddof, axis]) Calculate a one-way chi square test. count_tied_groups(x[, use_missing]) Counts the number of tied values. describe(a[, axis, ddof, bias]) Computes several descriptive statistics of the passed array. f_oneway(*args) Performs a 1-way ANOVA, returning an F-value and probability given any number of groups. find_repeats(arr) Find repeats in arr and return a tuple (repeats, repeat_count). friedmanchisquare(*args) Friedman Chi-Square is a non-parametric, one-way within-subjects ANOVA. kendalltau(x, y[, use_ties, use_missing]) Computes Kendall’s rank correlation tau on two variables x and y. kendalltau_seasonal(x) Computes a multivariate Kendall’s rank correlation tau, for seasonal data. kruskalwallis(*args) Compute the Kruskal-Wallis H-test for independent samples ks_twosamp(data1, data2[, alternative]) Computes the Kolmogorov-Smirnov test on two samples. kurtosis(a[, axis, fisher, bias]) Computes the kurtosis (Fisher or Pearson) of a dataset. kurtosistest(a[, axis]) Tests whether a dataset has normal kurtosis linregress(x[, y]) Calculate a linear least-squares regression for two sets of measurements. mannwhitneyu(x, y[, use_continuity]) Computes the Mann-Whitney statistic plotting_positions(data[, alpha, beta]) Returns plotting positions (or empirical percentile points) for the data. mode(a[, axis]) Returns an array of the modal (most common) value in the passed array. moment(a[, moment, axis]) Calculates the nth moment about the mean for a sample. mquantiles(a[, prob, alphap, betap, axis, limit]) Computes empirical quantiles for a data array. msign(x) Returns the sign of x, or 0 if x is masked. normaltest(a[, axis]) Tests whether a sample differs from a normal distribution. obrientransform(*args) Computes a transform on input data (any number of columns). pearsonr(x, y) Calculates a Pearson correlation coefficient and the p-value for testing non-correlation. plotting_positions(data[, alpha, beta]) Returns plotting positions (or empirical percentile points) for the data. pointbiserialr(x, y) Calculates a point biserial correlation coefficient and its p-value. rankdata(data[, axis, use_missing]) Returns the rank (also known as order statistics) of each data point along the given axis. scoreatpercentile(data, per[, limit, …]) Calculate the score at the given ‘per’ percentile of the sequence a. sem(a[, axis, ddof]) Calculates the standard error of the mean of the input array. skew(a[, axis, bias]) Computes the skewness of a data set. skewtest(a[, axis]) Tests whether the skew is different from the normal distribution. spearmanr(x, y[, use_ties]) Calculates a Spearman rank-order correlation coefficient and the p-value to test for non-correlation. theilslopes(y[, x, alpha]) Computes the Theil-Sen estimator for a set of points (x, y). tmax(a[, upperlimit, axis, inclusive]) Compute the trimmed maximum tmean(a[, limits, inclusive, axis]) Compute the trimmed mean. tmin(a[, lowerlimit, axis, inclusive]) Compute the trimmed minimum trim(a[, limits, inclusive, relative, axis]) Trims an array by masking the data outside some given limits. trima(a[, limits, inclusive]) Trims an array by masking the data outside some given limits. trimboth(data[, proportiontocut, inclusive, …]) Trims the smallest and largest data values. trimmed_stde(a[, limits, inclusive, axis]) Returns the standard error of the trimmed mean along the given axis. trimr(a[, limits, inclusive, axis]) Trims an array by masking some proportion of the data on each end. trimtail(data[, proportiontocut, tail, …]) Trims the data by masking values from one tail. tsem(a[, limits, inclusive, axis, ddof]) Compute the trimmed standard error of the mean. ttest_onesamp(a, popmean[, axis]) Calculates the T-test for the mean of ONE group of scores. ttest_ind(a, b[, axis, equal_var]) Calculates the T-test for the means of TWO INDEPENDENT samples of scores. ttest_onesamp(a, popmean[, axis]) Calculates the T-test for the mean of ONE group of scores. ttest_rel(a, b[, axis]) Calculates the T-test on TWO RELATED samples of scores, a and b. tvar(a[, limits, inclusive, axis, ddof]) Compute the trimmed variance variation(a[, axis]) Computes the coefficient of variation, the ratio of the biased standard deviation to the mean. winsorize(a[, limits, inclusive, inplace, axis]) Returns a Winsorized version of the input array. zmap(scores, compare[, axis, ddof]) Calculate the relative z-scores. zscore(a[, axis, ddof]) Calculate the z score of each value in the sample, relative to the sample mean and standard deviation. compare_medians_ms(group_1, group_2[, axis]) Compares the medians from two independent groups along the given axis. gmean(a[, axis, dtype]) Compute the geometric mean along the specified axis. hdmedian(data[, axis, var]) Returns the Harrell-Davis estimate of the median along the given axis. hdquantiles(data[, prob, axis, var]) Computes quantile estimates with the Harrell-Davis method. hdquantiles_sd(data[, prob, axis]) The standard error of the Harrell-Davis quantile estimates by jackknife. hmean(a[, axis, dtype]) Calculate the harmonic mean along the specified axis. idealfourths(data[, axis]) Returns an estimate of the lower and upper quartiles. kruskal(*args) Compute the Kruskal-Wallis H-test for independent samples ks_2samp(data1, data2[, alternative]) Computes the Kolmogorov-Smirnov test on two samples. median_cihs(data[, alpha, axis]) Computes the alpha-level confidence interval for the median of the data. meppf(data[, alpha, beta]) Returns plotting positions (or empirical percentile points) for the data. mjci(data[, prob, axis]) Returns the Maritz-Jarrett estimators of the standard error of selected experimental quantiles of the data. mquantiles_cimj(data[, prob, alpha, axis]) Computes the alpha confidence interval for the selected quantiles of the data, with Maritz-Jarrett estimators. rsh(data[, points]) Evaluates Rosenblatt’s shifted histogram estimators for each data point. sen_seasonal_slopes(x) trimmed_mean(a[, limits, inclusive, …]) trimmed_mean_ci(data[, limits, inclusive, …]) Selected confidence interval of the trimmed mean along the given axis. trimmed_std(a[, limits, inclusive, …]) trimmed_var(a[, limits, inclusive, …]) ttest_1samp(a, popmean[, axis]) Calculates the T-test for the mean of ONE group of scores. brunnermunzel(x, y[, alternative, distribution]) Computes the Brunner-Munzel test on samples x and y

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