COVAR_SAMP
Reference material for 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
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
Returns
473291.6614849927
Example
But CurrentLevel
and CurrentScore
variables are not correlated at all
Returns
0.040317002824098884