NULL - that input row is ignored.
Use this when analyzing an entire population rather than a sample. For covariance of sample see COVAR_SAMP.
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.
Syntax
Parameters
Return Type
COVAR_POP returns a result of type DOUBLE PRECISION.
Examples
Theplaystats table contains the following data:
Rows: 5Execution time: 4.19ms
currentscore and currentlevel in the playstats table are highly correlated, producing a large covariance value:
Rows: 1Execution time: 7.65ms
currentlevel and currentspeed are not correlated, so their covariance is close to zero:
Rows: 1Execution time: 6.90ms