# Analysis Tools

• ### Contingency Tables (with Calculator)

This page introduces contigency tables with an example on fruit trees and fire blight. Two calculators are provided that allow users to enter their own contigency table and test for treatment effects. The first calculator performs Fisher's Exact Test on a 2x2 tables. The second performs a chi-square test on up to a 9x9 table.

• ### Bayesian Calculator

This page uses Bayes' Theorem to calculate the probability of a hypothesis given a datum. An example about cancer is given to help users understand Bayes' Theorem and the calculator. Key Word: Conditional Probability.
• ### Statistical Tables Calculator

This page provides distribution calculators for the binomial, normal, Student's T, Chi-square, and Fisher's F distributions. Users set the parameters and enter either the probability or the test statistic and the calculators return the missing value.
• ### Analysis Tool: T-Distribution Table

This page provides a t-table with degrees of freedom 1-30, 60, 120, and infinity and seven levels of alpha from .1 to .0005.

• ### Analysis Tool: Normal Distribution Table

This page provides a z-table with alpha levels from .00 to .09.

• ### Analysis Tool: F Distribution Tables

This page provides a table of F distribution probabilities for alpha = 0.10, 0.05, 0.025, and 0.01.

• ### Analysis Tool: One- and Two-Way Analysis of Variance (ANOVA) JAVA Applet

This applet allows users to input their own data and perform one- and two-way Analyses of Variance. Key Word: ANOVA.

• ### Wilcoxon-Mann-Whitney U Test JAVA Calculator

This calculator determines the level of significance for the Wilcoxon-Mann-Whitney U-statistic. Users can enter N1, N2, and U or simply enter the raw data.

• ### Analysis Tool: Chi-Square Test for Known Distributions

This test checks whether an observed distribution differs from an expected distribution. It computes the chi-square statistic, degrees of freedom (DoF), and p-value. Users input a table with row and column labels, observed frequencies on the first row, and expected frequencies on the second row. The null hypothesis is that the observed values have the expected frequency distribution.