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• Analysis Tool: Chi-Square "Goodness of Fit"

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.

• Analysis Tool: Pascal (Negative Binomial) Probabilities (For Sequential Sampling)

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.

• Analysis Tool: Point Biserial Correlation Coefficient

This page calculates the point biserial correlation coefficient for the case where one variable is dichotomous and the other is non-dichotomous. This page allows the user to input the data directly or copy and paste from a spreadsheet application and provides data summary.

• Analysis Tool: Fitting an Observed Frequency Distribution to the Closest Poisson Distribution

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.

• Poisson Distribution

This lesson on the Poisson distribution explains the theory, history, and applications of the distribution and gives examples and a multiple choice test.
• Analysis Tool: The Confidence Interval of a Proportion

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.

• Analysis Tool: The Confidence Interval for Two Independent Proportions

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.

• Analysis Tool: McNemar's Test (Significance of Difference between Correlated Proportions)

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.

• Analysis Tool: Significance of Difference Between Independent Proportions

Calculates the z-ratio and associated one-tail and two-tail probabilities for the difference between two independent proportions.

• Analysis Tool: Significance of Difference Between Correlation Coefficients

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.