Updated: 28 February 2011

*Note: This documentation is for the SQL2008 (and later) version of this XLeratorDB function, it is not compatible with SQL Server 2005.*

Click here for the SQL2005 version of the VAR function

Use VAR to estimate variance based on a sample, or use VAR to estimate the population variance from a sample of

The equation for VAR is:

the values used in the variance calculation. *@x* is an expression of type **float** or of a type that can be implicitly converted to **float**.

float

· If you want measure the population variance, then use the VARP function.

· NULL values are not included in the standard deviation calculation.

· VAR is an aggregate function and follows the same conventions as all other aggregate functions in SQL Server.

· If you have previously used the VAR scalar function, the VAR aggregate has a different syntax. The VAR scalar function is no longer available in XLeratorDB/statistics2008, though you can still use the scalar VAR _q.

SELECT wct.VAR(x) as VAR

FROM (VALUES

(91.3698),

(76.3382),

(74.5692),

(85.2957),

(99.0112),

(86.99),

(70.7837),

(72.834),

(78.1644),

(77.7472),

(66.0627),

(59.781),

(68.4793),

(78.6103),

(59.8621)

) n(x)

This produces the following result

VAR

----------------------

121.668645804095

(1 row(s) affected)

In the following example, we will use demonstrate the difference between the SQL Server VAR calculation and XLeratorDB VAR calculation. Using the

SELECT ROUND(SeriesValue * 10, 0) + 1000000000 as x

INTO #n

FROM wctMath.wct.SERIESFLOAT(0,1,'',1000000,'R')

SELECT VAR(x) as [SQL SERVER VAR]

,wct.VAR(x) as [XLDB VAR]

,VAR(x-1000000000) as [SQL SERVER VAR adjusted]

FROM #n

DROP TABLE #n

This produces the following result. Your results will vary, since the dataset is randomly generated. The first column shows the built-in SQL Server VAR calculation, the second shows the XLeratorDB calculation and the third column shows the built-in calculation for x minus 1,000,000,000.

SQL SERVER VAR XLDB VAR SQL SERVER VAR adjusted

---------------------- ---------------------- -----------------------

277965.192653193 8.49671095762951 8.49671095774196

(1 row(s) affected)