When it comes to statistical analysis, SQL Server may not be the first platform that comes to mind. However, with the help of XLeratorDB, a powerful statistical function library, SQL Server users can now perform sophisticated statistical analysis right within their database.

SQL Server has a limited number of built-in statistical functions, unlike Oracle which offers a wide range of statistical functions. This is where XLeratorDB comes in, bridging the gap by providing over 300 statistical and mathematical functions. These functions include calculations for moments about the mean, distribution functions, linear regression, correlation, statistical inference, interpolation, and many more.

Let’s take a look at a simple example of how XLeratorDB can be used for statistical analysis in SQL Server. Suppose we want to predict the demand for certain products based on previous year’s unit sales. We can start by creating a table and populating it with the necessary data:

SELECT *
INTO #n
FROM (VALUES
(856018,'widget 1',2008,92303),
(856018,'widget 1',2009,172259),
(856018,'widget 1',2010,262942),
(856018,'widget 1',2011,356509),
(856018,'widget 1',2012,441066),
(856018,'widget 1',2013,536820),
...
) n([Product ID],[Description],[Year],[Units])

Now, we can use the XLeratorDB FORECAST function to predict the unit demand for 2014 based on the actual units from 2008 through 2013. We can also include the R-squared value, which indicates the goodness-of-fit for the prediction:

SELECT
[Product ID],
[Description],
ROUND(wct.FORECAST(2014,[Units],[Year]), 0) AS Forecast,
wct.RSQ([Units],[Year]) AS [R-squared]
FROM
#n
GROUP BY
[Product ID],
[Description]

In addition to the predicted values for 2014, we can also see the associated R-squared values. A higher R-squared value indicates a better fit of the least-squares line.

To present the forecast alongside the historical results for each year, we can pivot the results:

SELECT
[Product Id],
[Description],
[2008],
[2009],
[2010],
[2011],
[2012],
[2013],
[2014]
FROM (
SELECT
*
FROM
#n
UNION ALL
SELECT
[Product ID],
[Description],
2014,
ROUND(wct.FORECAST(2014,[Units],[Year]), 0) AS Units
FROM
#n
GROUP BY
[Product ID],
[Description]
) D
PIVOT(
SUM([units])
FOR
[Year]
IN
([2008],[2009],[2010],[2011],[2012],[2013],[2014])
) P

Now, we can see the predicted values for 2014 alongside the historical values for 2008 through 2014.

Performing statistical analysis in SQL Server has several advantages. It allows for predictions at various levels of detail, such as by region, state, city, or even store address, with only minor changes to the SQL query. This type of analysis is also fast and scalable, making it possible to make predictions in real-time. For example, the FORECAST function in XLeratorDB can handle large datasets with ease.

It’s worth noting that XLeratorDB also addresses the issue of numeric instability in some of the built-in SQL Server functions. For example, the STDEVP function in SQL Server may produce inaccurate results in certain cases. However, using the XLeratorDB STDEV_P function can provide more reliable and accurate results.

If you’re interested in exploring statistical analysis and predictive analytics in SQL Server, I highly recommend downloading the 15-day free trial of XLeratorDB. It’s a powerful tool that can unlock the full potential of statistical analysis within your SQL Server environment.