Correl() returns the aggregated correlation coefficient for two data sets. The correlation function is a measure of the relationship between the data sets and is aggregated for (x,y) value pairs iterated over the chart dimensions.
Syntax:
Correl([{SetExpression}] [DISTINCT] [TOTAL [<fld{, fld}>]] value1, value2 )
Return data type: numeric
Arguments:
| Argument | Description |
|---|---|
| value1, value2 | The expressions or fields containing the two sample sets for which the correlation coefficient is to be measured. |
| SetExpression | By default, the aggregation function will aggregate over the set of possible records defined by the selection. An alternative set of records can be defined by a set analysis expression. |
| DISTINCT | If the word DISTINCT occurs before the function arguments, duplicates resulting from the evaluation of the function arguments are disregarded. |
| TOTAL |
If the word TOTAL occurs before the function arguments, the calculation is made over all possible values given the current selections, and not just those that pertain to the current dimensional value, that is, it disregards the chart dimensions. The TOTAL qualifier may be followed by a list of one or more field names within angle brackets <fld>. These field names should be a subset of the chart dimension variables. See also: Defining the aggregation scope |
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in combination with calculated dimensions.
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair being disregarded.
Examples and results:
| Example | Result |
|---|---|
| Correl(Age, Salary) |
For a table including the dimension |
| Correl(TOTAL Age, Salary)) |
0.927. This and the following results are shown to three decimal places for readability. If you create a filter panel with the dimension Gender, and make selections from it, you see the result 0.951 when Female is selected and 0.939 if Male is selected. This is because the selection excludes all results that do not belong to the other value of Gender. |
| Correl({1} TOTAL Age, Salary)) |
0.927. Independent of selections. This is because the set expression {1} disregards all selections and dimensions. |
| Correl(TOTAL <Gender> Age, Salary)) |
0.927 in the total cell, 0.939 for all values of Male, and 0.951 for all values of Female. This corresponds to the results from making the selections in a filter panel based on Gender. |
Data used in examples:
Salary:
LOAD * inline [
"Employee name"|Gender|Age|Salary
Aiden Charles|Male|20|25000
Brenda Davies|Male|25|32000
Charlotte Edberg|Female|45|56000
Daroush Ferrara|Male|31|29000
Eunice Goldblum|Female|31|32000
Freddy Halvorsen|Male|25|26000
Gauri Indu|Female|36|46000
Harry Jones|Male|38|40000
Ian Underwood|Male|40|45000
Jackie Kingsley|Female|23|28000
] (delimiter is '|');
Learn more
Did this page help you?
If you find any issues with this page or its content – a typo, a missing step, or a technical error – let us know how we can improve!