This page will perform an analysis of variance for the situation where there are three independent variables, A, B, and C, each with two levels. The user may enter data directly or copy and paste from a spreadsheet or other application.
This page will perform a two-way factorial analysis of variance for designs in which there are 2-4 randomized blocks of matched subjects, with 2-4 repeated measures for each subject.
In the first simulation, random samples of size n are drawn from the population one sample at a time. With df=3, the critical value of chi-square for significance at or beyond the 0.05 level is 7.815; hence, any calculated value of chi-square equal to or greater than 7.815 is recorded as "significant," while any value smaller than that is noted as "non-significant." The second simulation does the same thing, except that it draws random samples 100 at a time. The Power of the Chi-Square "Goodness of Fit" Test pertains to the questionable common practice of accepting the null hypothesis upon failing to find a significant result in a one- dimensional chi-square test.
Given a sample of N values of X randomly drawn from a normally distributed population, this page will calculate the .95 and .99 confidence intervals (CI) for the estimated mean of the population.
The following pages calculate r, r-squared, regression constants, Y residuals, and standard error of estimate for a set of N bivariate values of X and Y, and perform a t-test for the significance of the obtained value of r. Allows for import of raw data from a spreadsheet; for samples of any size, large or small.
This page will calculate r_s , the Spearman rank- order correlation coefficient, for a bivariate set of paired XY rankings. As the page opens, you will be prompted to enter the number of items for which there are paired rankings. If you are starting out with raw (unranked) data, the necessary rank-ordering will be performed automatically.