Calculates the z-ratio and associated one-tail and two-tail probabilities for the difference between two correlated proportions, such as might be found in the case where the proportions are based on the same sample of subjects or on matched samples.
Calculates the z-ratio and associated one-tail and two-tail probabilities for the difference between two correlated proportions, such as might be found in the case where the proportions are based on the same sample of subjects or on matched samples.
To assess the significance of any particular instance of r, enter the values of N[>6] and r into the designated cells, then click the 'Calculate' button. Application of this formula to any particular observed sample value of r will accordingly test the null hypothesis that the observed value comes from a population in which rho=0.
This page will perform a t-test for the significance of the difference between the observed mean of a sample and a hypothetical mean of the population from which the sample is randomly drawn. The user will be asked to specify the sample size as the page opens.
Beginning with a set of n paired values of Xa and Xb, this page will perform the necessary rank- ordering along with all other steps appropriate to the Wilcoxon test. As the page opens, you will be prompted to enter the number of paired values of Xa and Xb.
In this applet, we simulate a series of hypothesis of tests for the value of the parameter p in a Bernoulli random variable. Each column of red and green marks represents a sample of 30 observations. "Successes'' are coded by green marks and "failures'' by red marks.
This site has a wide collection of statistical resources inluding an online textbook covering first-year non-calculus based statistics (e.g. Normal distribution, ANOVA, Chi-Square), a simulation/demonstration section containing Java Applets on these first-year topics (ANOVA, Binomial Distribution,Central Limit Theorem, Chi Square, Confidence Interval, Correlation, Central Tendency, Effect Size, Goodness of Fit, Histogram, Normal Distribution, Power, Regression, Repeated Measures, Restriction of Range, Sampling Distribution, Skew, t-test, Transformations), and case studies covering the topics in the first-year statistics course. There is also a page with some basic statistical analysis tools that will aid in doing the computations if you have a Java enabled browser. The source code for these resources can also be downloaded from this site.
The following pages calculate r, r-squared, regression constants, Y residuals, and standard error of estimate for a set of N bivariate values of X and Y, and perform a t-test for the significance of the obtained value of r. Values of X and Y are entered directly into individual data cells. This page will also work with samples of any size, though it will be rather unwieldy with samples larger than about N=50. As the page opens, you will be prompted to enter the value of N.
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.
This page will calculate the intercorrelations (r and r2) for up to five variables, designated as A, B, C, D, and E.
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.