Resource Library

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.

This page will calculate the first- and second-order partial correlations for four intercorrelated variables, W, X, Y, and Z. If you enter a value of N (providing N>9), the program will also calculate the values of t along with the associated two-tailed probability values.

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.

In the Latin Square computational pages on this site, the third IV, with levels designated as A, B, C, etc., is listed as the "treatment" variable. The analysis of variance within an orthogonal Latin Square results in three F-ratios: one for the row variable, one for the column variable, and one for the third IV whose j levels are distributed orthogonally among the cells of the rows x columns matrix.

In the Latin Square computational pages on this site, the third IV, with levels designated as A, B, C, etc., is listed as the "treatment" variable. The analysis of variance within an orthogonal Latin Square results in three F-ratios: one for the row variable, one for the column variable, and one for the third IV whose j levels are distributed orthogonally among the cells of the rows x columns matrix.

This page has two calculators. One will cacluate a simple logistic regression, while the other calculates the predicted probability and odds ratio. There is also a brief tutorial covering logistic regression using an example involving infant gestational age and breast feeding. Please note, however, that the logistic regression accomplished by this page is based on a simple, plain-vanilla empirical regression.

This page will calculate the intercorrelations (r and r2) for up to five variables, designated as A, B, C, D, and E.

This page will calculate the intercorrelations (r) for any number of variables (V1, V2, V3, etc.) and for any number of observations per variable.