This page will perform basic multiple regression analysis for the case where there are several independent predictor variables, X1, X2, etc., and one dependent or criterion variable, Y. Requires import of data from a spreadsheet.
This page will perform basic multiple regression analysis for the case where there are several independent predictor variables, X1, X2, etc., and one dependent or criterion variable, Y. Requires import of data from a spreadsheet.
This calculator performs the following for a contingency table up to 5x5: chi-square analysis; Cramer's V; two asymmetrical versions of lambda; the Goodman-Kruskal index of predictive association; other measures relevant to categorical prediction. Key Word: Categorical Analysis.
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.
Given three overlapping correlation coefficients: rXY, rXZ, and rYZ, this page will calculate the first-order partial correlations: rXY.Z, rXZ.Y, and rYZ.X. If you enter the value of N (providing N>6), the program will also calculate the values of t for the partial correlations (df=N-3) along with the associated two-tailed probability values.
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.
This page calculates the Poisson distribution that most closely fits an observed frequency distribution, as determined by the method of least squares. Users enter observed frequencies, and the page returns the fitted Poisson frequencies, the mean and variance of the observed distribution and the fitted Poisson distribution, and R-squared.
This page will calculate the lower and upper limits of the 95% confidence interval for a proportion, according to two methods described by Robert Newcombe, both derived from a procedure outlined by E. B. Wilson in 1927. The first method uses the Wilson procedure without a correction for continuity; the second uses the Wilson procedure with a correction for continuity.