This page will perform a two-way factorial analysis of variance for designs in which there are 2-4 levels of each of two variables, A and B, with each subject measured under each of the AxB combinations.
This page will perform a two-way factorial analysis of variance for designs in which there are 2-4 levels of each of two variables, A and B, with each subject measured under each of the AxB combinations.
This page will compute the Two-Way Factorial ANOVA for Independent Samples, for up to four rows by four columns. This page will also calculate the critical values of Tukey's HSD for purposes of post-ANOVA comparisons.
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.
This page will calculate the lower and upper limits of the 95% confidence interval for the difference between two independent proportions, 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.
Given two independent samples of sizes n_a and n_b, this page will estimate the significance of the difference between the means of the samples, based on multiple random re-sortings of the values that have been entered for samples A and B. As the page opens, you will be prompted for the sizes of the two samples.
This page will perform the procedure for up to k=12 sample values of r, with a minimum of k=2. It will also perform a chi-square test for the homogeneity of the k values of r, with df=k-1. The several values of r can be regarded as coming from the same population only if the observed chi-square value proves the be non-significant.
This calculator returns the value of t for the difference between the means of two correlated samples, for sample sizes up to 10. Users are prompted for sample size as the page opens. It will also calculate various summary statistics for the two samples.
This chapter of the "Concepts and Applications of Inferential Statistics" online textbook describes in detail the Kruskal-Wallis test, it's formulas, variables, and procedures using an example involving wine-tasters.
As the page opens, you will be prompted to enter two sample size values, na and nb. If the samples are of different sizes, the larger of the two should be designated as sample A. If you are starting out with raw (unranked) data, the necessary rank- ordering will be performed automatically.