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The Pearson correlation measures the linear relationship between two variables. Results range from -1 to +1 inclusive, where 1 denotes an exact positive linear relationship, 0 denotes no linear relationship between the variance and −1 is an exact negative relationship.

The square of a CORR result is equivalent to the R-Squared value for a linear trend line model. See Trend Line Model Terms(Link opens in a new window).

Use with table-scoped LOD expressions:

You can use CORR to visualise correlation in a disaggregated scatter using a table-scoped level of detail expression(Link opens in a new window). For example:

{CORR(Sales, Profit)}

With a level of detail expression, the correlation is run over all rows. If you used a formula like CORR(Sales, Profit) (without the surrounding brackets to make it a level of detail expression), the view would show the correlation of each individual point in the scatter plot with each other point, which is undefined.

CORR is available with the following data sources: Tableau data extracts, Cloudera Hive, EXASolution, Firebird (version 3.0 and later), Google BigQuery, Hortonworks Hadoop Hive, IBM PDA (Netezza), Oracle, PostgreSQL, Presto, SybaseIQ, Teradata, Vertica.

For other data sources, consider either extracting the data or using WINDOW_CORR. See Table Calculation Functions(Link opens in a new window).

Covariance quantifies how two variables change together. A positive covariance indicates that the variables tend to move in the same direction, as when larger values of one variable tend to correspond to larger values of the other variable, on average. Sample covariance uses the number of non-null data points n-1 to normalise the covariance calculation, rather than n, which is used by the population covariance (available with the COVARP function). Sample covariance is the appropriate choice when the data is a random sample that is being used to estimate the covariance for a larger population.

If <expression1> and <expression2> are the same, for example COVAR([profit], [profit]), COVAR returns a value that indicates how widely values are distributed.

The value of COVAR(X, X) is equivalent to the value of VAR(X) and also to the value of STDEV(X)^2.

COVAR is available with the following data sources: Tableau data extracts, Cloudera Hive, EXASolution, Firebird (version 3.0 and later), Google BigQuery, Hortonworks Hadoop Hive, IBM PDA (Netezza), Oracle, PostgreSQL, Presto, SybaseIQ, Teradata, Vertica.

For other data sources, consider either extracting the data or using WINDOW_COVAR. See Table Calculation Functions(Link opens in a new window).

Covariance quantifies how two variables change together. A positive covariance indicates that the variables tend to move in the same direction, as when larger values of one variable tend to correspond to larger values of the other variable, on average. Population covariance is sample covariance multiplied by (n-1)/n, where n is the total number of non-null data points. Population covariance is the appropriate choice when there is data available for all items of interest as opposed to when there is only a random subset of items, in which case sample covariance (with the COVAR function) is appropriate.

If <expression1> and <expression2> are the same, for example COVARP([profit], [profit]), COVARP returns a value that indicates how widely values are distributed. Note: The value of COVARP(X, X) is equivalent to the value of VARP(X) and also to the value of STDEVP(X)^2.

COVARP is available with the following data sources: Tableau data extracts, Cloudera Hive, EXASolution, Firebird (version 3.0 and later), Google BigQuery, Hortonworks Hadoop Hive, IBM PDA (Netezza), Oracle, PostgreSQL, Presto, SybaseIQ, Teradata, Vertica

For other data sources, consider either extracting the data or using WINDOW_COVAR. See Table Calculation Functions(Link opens in a new window).

Returns the maximum of the two arguments, which must be of the same data type.

MAX can also be applied to a single field as an aggregation.

MAX(4,7) = 7
MAX(#3/25/1986#, #2/20/2021#) = #2/20/2021# 
MAX([Name]) = "Zander"

For strings

MAX is usually the value that comes last in alphabetical order.

For database data sources, the MAX string value is highest in the sort sequence defined by the database for that column.

For dates

For dates, the MAX is the most recent date. If MAX is an aggregation, the result will not have a date hierarchy. If MAX is a comparison, the result will retain the date hierarchy.

As an aggregation

MAX(expression) is an aggregate function and returns a single aggregated result. This displays as AGG(expression) in the viz.

As a comparison

MAX(expr1, expr2) compares the two values and returns a row-level value.

See also MIN.

MEDIAN is not available for the following data sources: Access, Amazon Redshift, Cloudera Hadoop, HP Vertica, IBM DB2, IBM PDA (Netezza), Microsoft SQL Server, MySQL, SAP HANA, Teradata.

For other data source types, you can extract your data into an extract file to use this function. See Extract Your Data(Link opens in a new window).

Returns the minimum of the two arguments, which must be of the same data type.

MIN can also be applied to a single field as an aggregation.

MIN(4,7) = 4
MIN(#3/25/1986#, #2/20/2021#) = #3/25/1986#
MIN([Name]) = "Abebi"

For strings

MIN is usually the value that comes first in alphabetical order.

For database data sources, the MIN string value is lowest in the sort sequence defined by the database for that column.

For dates

For dates, the MIN is the earliest date. If MIN is an aggregation, the result will not have a date hierarchy. If MIN is a comparison, the result will retain the date hierarchy.

As an aggregation

MIN(expression) is an aggregate function and returns a single aggregated result. This displays as AGG(expression) in the viz.

As a comparison

MIN(expr1, expr2) compares the two values and returns a row-level value.

See also MAX.

PERCENTILE([Score], 0.9)

This function is available for the following data sources: Non-legacy Microsoft Excel and Text File connections, Extracts and extract-only data source types (for example, Google Analytics, OData or Salesforce), Sybase IQ 15.1 and later data sources, Oracle 10 and later data sources, Cloudera Hive and Hortonworks Hadoop Hive data sources, EXASolution 4.2 and later data sources.

For other data source types, you can extract your data into an extract file to use this function. See Extract Your Data(Link opens in a new window).