Save the analysis and press F6 to
execute it.
An information pop-up opens to confirm that the operation is in progress and
the analysis editor switches to the Analysis Results
view.
This functional dependency analysis evaluated the records present in the
city column and those present in the
state_province column against each other to see if the
city names match to the listed state names and vice versa. The returned results,
in the %Match column, indicate the functional
dependency strength for each determinant column. The records that do not match
are indicated in red.
The #Match column in the result table lists
the numbers of the distinct determinant values in each of the analyzed columns.
The #row column in the analysis results lists
the actual relations between the determinant attribute and the dependant
attribute. In this example, #Match in the first
row of the result table represents the number of distinct cities, and #row represents the number of distinct pairs (city,
state_province). Since these two numbers are not equal, then the functional
dependency relationship here is only partial and the ratio of the numbers
(%Match) measures the actual dependency
strength. When these numbers are equal, you have a "strict" functional
dependency relationship, that is to say each city appears only once with each
state.
Information noteNote: The presence of null values in either of the two analyzed columns will lessen
the "dependency strength". The system does not ignore null values, but
rather calculates them as values that violates the functional
dependency.