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.
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. Values of X and Y are entered directly into individual data cells. This page will also work with samples of any size, though it will be rather unwieldy with samples larger than about N=50. As the page opens, you will be prompted to enter the value of N.
Nonparametric test for the significance of the difference among the distributions of k correlated samples (A, B, etc., each of size n) involving repeated measures or matched sets. As the page opens, you will be prompted to enter the value of n. The necessary rank- ordering of your raw data will be performed automatically.
Nonparametric test for the significance of the difference among the distributions of k correlated samples (A, B, etc., each of size n) involving repeated measures or matched sets. As the page opens, you will be prompted to enter the value of n. The necessary rank- ordering of your raw data will be performed automatically.
As the page opens, you will be prompted to enter the sizes of your several samples. If you are starting out with raw (unranked) data, the necessary rank- ordering will be performed automatically.
As the page opens, you will be prompted to enter the sizes of your several samples. 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.