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

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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

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Syntax

COVAR_POP(<expr1>, <expr2>)

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Parameters

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

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Return Type

COVAR_POP returns a result of type DOUBLE PRECISION.

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Example

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Examples

The code examples use PlayStats table from the sample UltraFast database. Example The CurrentLevel and CurrentScore variables are highly correlated 
SELECT COVAR_POP(CurrentScore, CurrentLevel) FROM PlayStats
Returns 473291.6610282666 Example But CurrentLevel and CurrentScore variables are not correlated at all 
SELECT COVAR_POP(CurrentLevel, CurrentSpeed) FROM PlayStats
Returns 0.040317002785193