Calculates for each pair of selected columns a correlation coefficient, i.e. a measure of the correlation of the two variables.

Which correlation measure is applied depends on the types of the underlying variables:
numeric <-> numeric : Pearson's product-moment coefficient . Missing values in a column are ignored in such a way that for the computation of the correlation between two columns only complete records are taken into account. For instance, if there are three columns A, B and C and a row contains a missing value in column A but not in B and C, then the row will be ignored for computing the correlation between (A, B) and (A, C). It will not be ignored for the correlation between (B, C). This corresponds to the function cor(<data.frame>, use="pairwise.complete.obs") in the R statistics package.
The value of this measure ranges from -1 (strong negative correlation) to 1 (strong positive correlation). A value of 0 represents no linear correlation (the columns might still be highly dependent on each other, though).
The p-value for these columns indicates the probability of an uncorrelated system producing a correlation at least as extreme, if the mean of the correlation is zero and it follows a t-distribution with df degrees of freedom.
nominal <-> nominal : Pearson's chi square test on the contingency table . This value is then normalized to a range [0,1] using Cramer's V , whereby 0 represents no correlation and 1 a strong correlation. Missing values in nominal columns are treated such as they were a self-contained possible value. If one of the two columns contains more possible values than specified in the dialog (default 50), the correlation will not be computed.
The p-value for these columns indicates the probability of independent variables showing as extreme level of dependence. The value is the same as for a chi-square test of independence of variables in a contingency table.
Correlation measures for other pairs of columns are not available, they are represented by missing values in the output table and crosses in the accompanying view.