Are there any Linear Regression Function in SQL Server?

Question

Are there any Linear Regression Function in SQL Server 2005/2008, similar to the the Linear Regression functions in Oracle ?

Answer 1

I have translated the Linear Regression Function used in the funcion Forecast in Excel, and created an SQL function that returns a,b, and the Forecast. You can see the complete teorical explanation in the excel help for FORECAST fuction. Firs of all you will need to create the table data type XYFloatType:

 CREATE TYPE [dbo].[XYFloatType] 
AS TABLE(
[X] FLOAT,
[Y] FLOAT)

Then write the follow function:

    /*
-- =============================================
-- Author:      Me      :)
-- Create date: Today   :)
-- Description: (Copied Excel help): 
--Calculates, or predicts, a future value by using existing values. 
The predicted value is a y-value for a given x-value. 
The known values are existing x-values and y-values, and the new value is predicted by using linear regression. 
You can use this function to predict future sales, inventory requirements, or consumer trends.
-- =============================================
*/

CREATE FUNCTION dbo.FN_GetLinearRegressionForcast

(@PtXYData as XYFloatType READONLY ,@PnFuturePointint)
RETURNS @ABDData TABLE( a FLOAT, b FLOAT, Forecast FLOAT)
AS

BEGIN 
    DECLARE  @LnAvX Float
            ,@LnAvY Float
            ,@LnB Float
            ,@LnA Float
            ,@LnForeCast Float
    Select   @LnAvX = AVG([X])
            ,@LnAvY = AVG([Y])
    FROM @PtXYData;
    SELECT @LnB =  SUM ( ([X]-@LnAvX)*([Y]-@LnAvY) )  /  SUM (POWER([X]-@LnAvX,2))
    FROM @PtXYData;
    SET @LnA = @LnAvY - @LnB * @LnAvX;
    SET @LnForeCast = @LnA + @LnB * @PnFuturePoint;
    INSERT INTO @ABDData ([A],[B],[Forecast]) VALUES (@LnA,@LnB,@LnForeCast)
    RETURN 
END

/*
your tests: 

 (I used the same values that are in the excel help)
DECLARE @t XYFloatType 
INSERT @t VALUES(20,6),(28,7),(31,9),(38,15),(40,21)        -- x and y values
SELECT *, A+B*30 [Prueba]FROM dbo.FN_GetLinearRegressionForcast@t,30);
*/

Answer 2

To the best of my knowledge, there is none. Writing one is pretty straightforward, though. The following gives you the constant alpha and slope beta for y = Alpha + Beta * x + epsilon:

-- test data (GroupIDs 1, 2 normal regressions, 3, 4 = no variance)
WITH some_table(GroupID, x, y) AS
(       SELECT 1,  1,  1    UNION SELECT 1,  2,  2    UNION SELECT 1,  3,  1.3  
  UNION SELECT 1,  4,  3.75 UNION SELECT 1,  5,  2.25 UNION SELECT 2, 95, 85    
  UNION SELECT 2, 85, 95    UNION SELECT 2, 80, 70    UNION SELECT 2, 70, 65    
  UNION SELECT 2, 60, 70    UNION SELECT 3,  1,  2    UNION SELECT 3,  1, 3
  UNION SELECT 4,  1,  2    UNION SELECT 4,  2,  2),
 -- linear regression query
/*WITH*/ mean_estimates AS
(   SELECT GroupID
          ,AVG(x * 1.)                                             AS xmean
          ,AVG(y * 1.)                                             AS ymean
    FROM some_table
    GROUP BY GroupID
),
stdev_estimates AS
(   SELECT pd.GroupID
          -- T-SQL STDEV() implementation is not numerically stable
          ,CASE      SUM(SQUARE(x - xmean)) WHEN 0 THEN 1 
           ELSE SQRT(SUM(SQUARE(x - xmean)) / (COUNT(*) - 1)) END AS xstdev
          ,     SQRT(SUM(SQUARE(y - ymean)) / (COUNT(*) - 1))     AS ystdev
    FROM some_table pd
    INNER JOIN mean_estimates  pm ON pm.GroupID = pd.GroupID
    GROUP BY pd.GroupID, pm.xmean, pm.ymean
),
standardized_data AS                   -- increases numerical stability
(   SELECT pd.GroupID
          ,(x - xmean) / xstdev                                    AS xstd
          ,CASE ystdev WHEN 0 THEN 0 ELSE (y - ymean) / ystdev END AS ystd
    FROM some_table pd
    INNER JOIN stdev_estimates ps ON ps.GroupID = pd.GroupID
    INNER JOIN mean_estimates  pm ON pm.GroupID = pd.GroupID
),
standardized_beta_estimates AS
(   SELECT GroupID
          ,CASE WHEN SUM(xstd * xstd) = 0 THEN 0
                ELSE SUM(xstd * ystd) / (COUNT(*) - 1) END         AS betastd
    FROM standardized_data pd
    GROUP BY GroupID
)
SELECT pb.GroupID
      ,ymean - xmean * betastd * ystdev / xstdev                   AS Alpha
      ,betastd * ystdev / xstdev                                   AS Beta
FROM standardized_beta_estimates pb
INNER JOIN stdev_estimates ps ON ps.GroupID = pb.GroupID
INNER JOIN mean_estimates  pm ON pm.GroupID = pb.GroupID

Here GroupID is used to show how to group by some value in your source data table. If you just want the statistics across all data in the table (not specific sub-groups), you can drop it and the joins. I have used the WITH statement for sake of clarity. As an alternative, you can use sub-queries instead. Please be mindful of the precision of the data type used in your tables as the numerical stability can deteriorate quickly if the precision is not high enough relative to your data.

EDIT: (in answer to Peter's question for additional statistics like R2 in the comments)

You can easily calculate additional statistics using the same technique. Here is a version with R2, correlation, and sample covariance:

-- test data (GroupIDs 1, 2 normal regressions, 3, 4 = no variance)
WITH some_table(GroupID, x, y) AS
(       SELECT 1,  1,  1    UNION SELECT 1,  2,  2    UNION SELECT 1,  3,  1.3  
  UNION SELECT 1,  4,  3.75 UNION SELECT 1,  5,  2.25 UNION SELECT 2, 95, 85    
  UNION SELECT 2, 85, 95    UNION SELECT 2, 80, 70    UNION SELECT 2, 70, 65    
  UNION SELECT 2, 60, 70    UNION SELECT 3,  1,  2    UNION SELECT 3,  1, 3
  UNION SELECT 4,  1,  2    UNION SELECT 4,  2,  2),
 -- linear regression query
/*WITH*/ mean_estimates AS
(   SELECT GroupID
          ,AVG(x * 1.)                                             AS xmean
          ,AVG(y * 1.)                                             AS ymean
    FROM some_table pd
    GROUP BY GroupID
),
stdev_estimates AS
(   SELECT pd.GroupID
          -- T-SQL STDEV() implementation is not numerically stable
          ,CASE      SUM(SQUARE(x - xmean)) WHEN 0 THEN 1 
           ELSE SQRT(SUM(SQUARE(x - xmean)) / (COUNT(*) - 1)) END AS xstdev
          ,     SQRT(SUM(SQUARE(y - ymean)) / (COUNT(*) - 1))     AS ystdev
    FROM some_table pd
    INNER JOIN mean_estimates  pm ON pm.GroupID = pd.GroupID
    GROUP BY pd.GroupID, pm.xmean, pm.ymean
),
standardized_data AS                   -- increases numerical stability
(   SELECT pd.GroupID
          ,(x - xmean) / xstdev                                    AS xstd
          ,CASE ystdev WHEN 0 THEN 0 ELSE (y - ymean) / ystdev END AS ystd
    FROM some_table pd
    INNER JOIN stdev_estimates ps ON ps.GroupID = pd.GroupID
    INNER JOIN mean_estimates  pm ON pm.GroupID = pd.GroupID
),
standardized_beta_estimates AS
(   SELECT GroupID
          ,CASE WHEN SUM(xstd * xstd) = 0 THEN 0
                ELSE SUM(xstd * ystd) / (COUNT(*) - 1) END         AS betastd
    FROM standardized_data
    GROUP BY GroupID
)
SELECT pb.GroupID
      ,ymean - xmean * betastd * ystdev / xstdev                   AS Alpha
      ,betastd * ystdev / xstdev                                   AS Beta
      ,CASE ystdev WHEN 0 THEN 1 ELSE betastd * betastd END        AS R2
      ,betastd                                                     AS Correl
      ,betastd * xstdev * ystdev                                   AS Covar
FROM standardized_beta_estimates pb
INNER JOIN stdev_estimates ps ON ps.GroupID = pb.GroupID
INNER JOIN mean_estimates  pm ON pm.GroupID = pb.GroupID

EDIT 2 improves numerical stability by standardizing data (instead of only centering) and by replacing STDEV because of numerical stability issues. To me, the current implementation seems to be the best trade-off between stability and complexity. I could improve stability by replacing my standard deviation with a numerically stable online algorithm, but this would complicate the implementation substantantially (and slow it down). Similarly, implementations using e.g. Kahan(-Babuška-Neumaier) compensations for the SUM and AVG seem to perform modestly better in limited tests, but make the query much more complex. And as long as I do not know how T-SQL implements SUM and AVG (e.g. it might already be using pairwise summation), I cannot guarantee that such modifications always improve accuracy.