This page will calculate standard measures for Rates, Risk Ratio, Odds, Odds Ratio, and Log Odds. It will also 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 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.
This page will calculate the value of chi-square for a one- dimensional "goodness of fit" test, for up to 8 mutually exclusive categories labeled A through H. To enter an observed cell frequency, click the cursor into the appropriate cell, then type in the value. Expected values can be entered as either frequencies or proportions. Toward the bottom of the page is an option for estimating the relevant probability via Monte Carlo simulation of the multinomial sampling distribution.
For a situation in which independent binomial events are randomly sampled in sequence, this page will calculate (a) the probability that you will end up with exactly k instances of the outcome in question, with the final (kth) instance occurring on trial N; and (b) the probability that you will have to sample at least N events before finding the kth instance of the outcome.
Calculates the z-ratio and associated one-tail and two-tail probabilities for the difference between two independent proportions.
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 two correlation coefficients, r_a and r_b, found in two independent samples. If r_a is greater than r_b, the resulting value of z will have a positive sign; if r_a is smaller than r_b, the sign of z will be negative.
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.
Generate a graphic and numerical display of the properties of the Normal Distribution. For a unit normal distribution, with M=0 and SD=Œ±1, enter 0 and 1 at the prompt. For a distribution with M=100 and SD=Œ±15, enter 100 and 15. And so forth