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Performs t tests on the equality of means. It tests the hypothesis that a sample has a mean equal to a hypothesized value.
Compare the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different.
Tests that two samples have the same mean, assuming paired data.
Test whether the proportion of successes on a two-level categorical dependent variable significantly differs from a hypothesized value.
Performs tests on the equality of standard deviations (variances).It tests that the standard deviation of a sample is equal to a hypothesized value.
Performs tests on the equality of standard deviations (variances).
Compares proportion in one group to a specified population proportion.
Tests on the equality of proportions using large-sample statistics. It tests that a sample has the same proportion within two independent groups or two samples have the same proportion.
One way analysis of variance.
Levene's robust test statistic for the equality of variances and the two statistics proposed by Brown and Forsythe that replace the mean in Levene's formula with alternative location estimators. The first alternative replaces the mean with the median. The second alternative replaces the mean with the 10% trimmed mean.
Test whether the observed proportions for a categorical variable differ from hypothesized proportions
Examine if there is a relationship between two categorical variables.
Test if the proportions of 3 or more dichotomous variables are equal in the same population.
Tests whether the observations are serially independent i.e. whether they occur in a random order, by counting how many runs there are above and below a threshold. By default, the median is used as the threshold. A small number of runs indicates positive serial correlation; a large number indicates negative serial correlation.
Test if the proportions of two dichotomous variables are equal in the same population.