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
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
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).
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
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.
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