COVAR_SAMP

Computes the sample covariance between two numeric expressions. If either one of expressions is NULL - that input row is ignored. Use this when analyzing a sample rather than an entire population. For covariance of population see COVAR_POP.

Covariance vs Correlation

Covariance and correlation both describe how two variables change together, but they do so in different ways:

• Covariance measures the direction of the relationship between two variables:
  • Positive covariance: variables tend to increase together.
  • Negative covariance: one increases as the other decreases.
  • Its value is unbounded and depends on the units of the variables, making it hard to interpret on its own.
• Correlation, specifically Pearson correlation, standardizes the relationship:
  • It is the normalized version of covariance, giving a unitless measure between -1 and 1.
  • This makes it easier to compare the strength of relationships between different pairs of variables.

For information on correlation see CORR

Syntax

COVAR_SAMP(<expr1>, <expr2>)

Parameters

Parameter	Description	Supported input types
<expr1>	First numeric expression to use for covariance computation.	DOUBLE PRECISION
<expr2>	Second numeric expression to use for covariance computation.	DOUBLE PRECISION

Return Type

COVAR_SAMP returns a result of type DOUBLE PRECISION.

Example

Examples

The code examples use PlayStats table from the sample UltraFast database.

Example

The CurrentLevel and CurrentScore variables are highly correlated

SELECT COVAR_SAMP(CurrentScore, CurrentLevel) FROM PlayStats

Returns

473291.6614849927

Example

But CurrentLevel and CurrentScore variables are not correlated at all

SELECT COVAR_SAMP(CurrentLevel, CurrentSpeed) FROM PlayStats

Returns

0.040317002824098884