Below you will find complete descriptions and links to 6 different analytics calculators for computing correlation values.

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Compute the intraclass correlation coefficient (or ICC) for an analysis of variance (ANOVA) study, given the between-groups and within-groups mean square values, and the number of subjects in each group. The ICC is often used in ANOVA-based analytics studies to determine how strongly subjects or items within the same group resemble one another.

Compute the Pearson correlation coefficient r between two variables, given the covariance value between the two variables, and each variable's standard deviation. Correlation coefficients are often very useful for understanding and comparing the relationships among variables in an analytics study.

Compute the one-tailed and two-tailed probability values for a Pearson correlation coefficient, given the sample size and the correlation value r. Knowing the significance level for a correlation coefficient is very useful for understanding the relationship between two variables in an analytics study.

Compute the t-value and degrees of freedom for a Pearson correlation coefficient, given the sample size and the value of the correlation coefficient r. Knowing the t-value and degrees of freedom that are associated with a particular correlation coefficient can be useful when comparing correlations or when seeking to conduct additional analyses in an analytics study.

Compute the covariance value between two variables, given the Pearson correlation coefficient for the two variables, and each variable's standard deviation. Covariances reveal the extent to which the values of two variables change together, which is often very useful to know when examining the relationships among variables in an analytics study.

Compute the extent to which two correlation coefficients are significantly different from one another, given the values of the two correlation coefficients and their associated sample sizes. The calculator computes the z-score for the significance test and the p-value. Knowing whether two correlation coefficients are significantly different from one another can be very useful in analytics studies that compare multiple groups.