 anova | % Perform a one-way analysis of variance (ANOVA). The goal is to test |
| bartlett_test | % Perform a Bartlett test for the homogeneity of variances in the data |
| chisquare_test_homogeneity | % Given two samples @var{x} and @var{y}, perform a chisquare test for |
| chisquare_test_independence | % Perform a chi-square test for independence based on the contingency |
| cor_test | % Test whether two samples @var{x} and @var{y} come from uncorrelated |
| f_test_regression | % Perform an F test for the null hypothesis rr * b = r in a classical |
| hotelling_test | % For a sample @var{x} from a multivariate normal distribution with unknown |
| hotelling_test_2 | % For two samples @var{x} from multivariate normal distributions with |
| kolmogorov_smirnov_test | % Perform a Kolmogorov-Smirnov test of the null hypothesis that the |
| kolmogorov_smirnov_test_2 | % Perform a 2-sample Kolmogorov-Smirnov test of the null hypothesis |
| kruskal_wallis_test | % Perform a Kruskal-Wallis one-factor 'analysis of variance'. |
| manova | % Perform a one-way multivariate analysis of variance (MANOVA). The |
| mcnemar_test | % For a square contingency table @var{x} of data cross-classified on |
| prop_test_2 | % If @var{x1} and @var{n1} are the counts of successes and trials in |
| run_test | % Perform a chi-square test with 6 degrees of freedom based on the |
| sign_test | % For two matched-pair samples @var{x} and @var{y}, perform a sign test |
| t_test | % For a sample @var{x} from a normal distribution with unknown mean and |
| t_test_2 | % For two samples x and y from normal distributions with unknown means |
| t_test_regression | % Perform an t test for the null hypothesis @code{@var{rr} * @var{b} = |
| u_test | % For two samples @var{x} and @var{y}, perform a Mann-Whitney U-test of |
| var_test | % For two samples @var{x} and @var{y} from normal distributions with |
| welch_test | % For two samples @var{x} and @var{y} from normal distributions with |
| wilcoxon_test | % For two matched-pair sample vectors @var{x} and @var{y}, perform a |
| z_test | % Perform a Z-test of the null hypothesis @code{mean (@var{x}) == |
| z_test_2 | % For two samples @var{x} and @var{y} from normal distributions with |
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