Using the Fisher r-to-z transformation, this page will calculate a value of z that can be applied to assess the significance of the difference between r, the correlation observed within a sample of size n and rho, the correlation hypothesized to exist within the population of bivariate values from which the sample is randomly drawn. If r is greater than rho, the resulting value of z will have a positive sign; if r is smaller than rho, the sign of z will be negative.
To assess the significance of any particular instance of r, enter the values of N[>6] and r into the designated cells, then click the 'Calculate' button. Application of this formula to any particular observed sample value of r will accordingly test the null hypothesis that the observed value comes from a population in which rho=0.
This calculator returns the value of t for the difference between the means of two correlated samples, for sample sizes up to 10. Users are prompted for sample size as the page opens. It will also calculate various summary statistics for the two samples.
This page will perform a t-test for the significance of the difference between the observed mean of a sample and a hypothetical mean of the population from which the sample is randomly drawn. The user will be asked to specify the sample size as the page opens.
For a table of frequency data cross-classified according to two categorical variables, X and Y, each of which has two levels or subcategories, this page will calculate the Phi coefficient of association; perform a chi-square test of association, if the sample size is not too small; and perform the Fisher exact probability test, if the sample size is not too large. For intermediate values of n, the chi-square and Fisher tests will both be performed.
This page will compute the t-test for either correlated or independent samples. One may copy and paste data in or type the data in individually.
As the page opens, you will be prompted to enter two sample size values, na and nb. If the samples are of different sizes, the larger of the two should be designated as sample A. If you are starting out with raw (unranked) data, the necessary rank- ordering will be performed automatically.
These pages will perform an analysis of covariance for k independent samples, where the individual samples, A, B, etc., represent k quantitative or categorical levels of the independent variable; DV = the dependent variable of interest; and CV = the concomitant variable whose effects one wishes to bring under statistical control. The pages in this first batch require the direct entry of data, item by item, and as they open you will be prompted to enter the size of the largest of your several samples. The pages in this second batch allow for the import of data from a spreadsheet via copy and paste procedures.
These pages will perform a factorial analysis of covariance for RxC independent samples, cross-tabulated according to two independent variables, A and B, where A is the row variable and B the column variable; DV = the dependent variable of interest; and CV = the concomitant variable whose effects one wishes to bring under statistical control. As the pages open, you will be prompted to enter the size of the largest of your several samples.
Beginning with a set of n paired values of Xa and Xb, this page will perform the necessary rank- ordering along with all other steps appropriate to the Wilcoxon test. As the page opens, you will be prompted to enter the number of paired values of Xa and Xb.