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3.2. The Module ’Correlations Analysis’
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3.2 The Module ’Correlations Analysis’
3.2.1 Purpose and short description
The data exploration module ’Correlations Analysis’ serves to get an overview over the
dependencies and correlations between the different data fields within a data source. This
is done by creating a table of field-field contingency coefficients.
3.2.2 The tabular correlations view
The main part of the correlations analysis panel displays a table of field-field correlations. In the literature, there are many different definition of measures for correlation, for
example the linear correlations coefficient (or Pearson’s correlations coefficient) between
two nummeric data fields. Linear correlations coefficients have values between -1 (strong
negative correlation) and +1 (strong positive correlation).
In Synop Analyzer, we use another measure for correlation which can also be calculated
for pairs of textual data fields, or for a numeric and a textual field: the so-called adjusted
contingeny coefficient C as it is defined in http://en.wikipedia.org/wiki/Contingency_table. This quantity assumes values between 0 (=no correlation) and 1 (=maximum
correlation). The contingency coefficient is stronly related to the bivariate value-value
matrix of the two involved fields as it is created in the Synop Analyzer module Bivariate
Analysis: if one creates a bivariate value-value matrix for two data fields such that the
field with the higher number of different values is traced on the y-axis, then one can derive
from this matrix
• a contingency coefficient of 1 if and only of in each matrix row all cells except one
are completely empty and all data records fall into one single populated cell. When
displayed in the module Bivariate Analysis, the matrix would only show intensively
red colored cells with count=0 and one single intensively green colored cell per row.
• a contingency coefficient of 0 if and only if each matrix cell contains exactly the
number of data records which one could have expected from calculating the product
of the relative appearance frequencies of the column value and the row value. When
displayed in the module Bivariate Analysis, such a matrix would have only white
matrix cells.
In the following we show an example for a continceny table. The example uses the
sample data doc/sample_data/customers.txt and Synop Analyzer’s default settings
for importing the data.