• ### Analysis Tool: Friedman Test for k = 4

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.

• ### Analysis Tool: Kruskal-Wallis Test for K = 3

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.

• ### Analysis Tool: Kruskal-Wallis Test for K = 4

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.

• ### Analysis Tool: 4x4 Orthogonal Latin Square with Restricted Full Rank Model (One Measure per Cell)

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.

• ### Analysis Tool: 5x5 Orthogonal Latin Square for Restricted Full Rank Model (One Measure per Cell)

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.

• ### Analysis Tool: Simple Logistic Regression

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.

• ### Analysis Tool: Matrix of Intercorrelations (Direct-entry format for small samples)

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

• ### Analysis Tool: Matrix of Intercorrelations (Data-import format)

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

• ### P Values and Statistical Significance

This resource defines what a p-value is, why .05 is significant, and when to use it. It also covers related topics such as one-tailed/two-tailed tests and hypothesis testing.
• ### Analysis Tool: Create a Pie Chart

This resource defines a pie chart. It also allows the user to input values to create their own graphs. The user has control over the title, up to 15 slices, the color of each slice, and can choose a 3-D option.