TREND
Updated: 15 January 2011
Use the aggregate TREND function to calculate the values along a linear trend. TREND fits a straight line (using the method of least squares) to the known-y dataset and the known-x dataset. The equation for TREND is:
Then
TREND = (m * @new_x) + b
Syntax
SELECT [wctStatistics].[wct].[TREND] (
<@fmtTREND, nvarchar(4000),>)
The syntax has changed as of release 1.07 and the function has changed from a scalar to an aggregate. Please make the appropriate changes when you upgrade to 1.07.
Arguments
@fmtTREND
An nvarchar string formatted by the fmtTREND function containing the known-y and known-x and new-x values to be used by the TREND calculation.
Return Types
float
Remarks
· TREND is an AGGREGATE function and follows the same conventions as all other AGGREGATE functions in SQL Server.
· TREND is a one-pass solution. For a two-pass solution, use the scalar function TREND_q.
Examples
In this example, we calculate the trend for a single set of x- and y-values with a single new x value
SELECT wct.TREND(wct.fmtTREND(y, x, 12.5)) as TREND
FROM (
SELECT 0.75, 1 UNION ALL
SELECT 2.5, 2 UNION ALL
SELECT 6.75, 3 UNION ALL
SELECT 10, 4
) n(x,y)
This produces the following result
TREND
----------------------
4.79116945107399
(1 row(s) affected)
In this example, we will populate some temporary table with some historical financial information and then calculate the slope. First, create the table and put some data in it:
CREATE TABLE #c(
SYM NVARCHAR(5),
YE BIGINT,
REV FLOAT,
GPROF FLOAT,
OPINC FLOAT,
NETINC FLOAT
)
INSERT INTO #c VALUES('YHOO',2009,6460.32,3588.57,386.69,597.99)
INSERT INTO #c VALUES('YHOO',2008,72.5,4185.14,12.96,418.92)
INSERT INTO #c VALUES('YHOO',2007,6969.27,4130.52,695.41,639.16)
INSERT INTO #c VALUES('YHOO',2006,6425.68,3749.96,940.97,751.39)
INSERT INTO #c VALUES('YHOO',2005,5257.67,3161.47,1107.73,1896.23)
INSERT INTO #c VALUES('GOOG',2009,23650.56,14806.45,8312.19,6520.45)
INSERT INTO #c VALUES('GOOG',2008,21795.55,13174.04,5537.21,4226.86)
INSERT INTO #c VALUES('GOOG',2007,16593.99,9944.9,54.44,4203.72)
INSERT INTO #c VALUES('GOOG',2006,10604.92,6379.89,3550,3077.45)
INSERT INTO #c VALUES('GOOG',2005,6138.56,3561.47,2017.28,1465.4)
INSERT INTO #c VALUES('MSFT',2009,62484,509,24167,18760)
INSERT INTO #c VALUES('MSFT',2008,58437,46282,21225,14569)
INSERT INTO #c VALUES('MSFT',2007,60420,48822,22271,17681)
INSERT INTO #c VALUES('MSFT',2006,51122,40429,18438,14065)
INSERT INTO #c VALUES('MSFT',2005,44282,36632,16064,12599)
INSERT INTO #c VALUES('ORCL',2009,26820,21056,9062,6135)
INSERT INTO #c VALUES('ORCL',2008,23252,18458,8321,5593)
INSERT INTO #c VALUES('ORCL',2007,22430,17449,7844,5521)
INSERT INTO #c VALUES('ORCL',2006,17996,13805,5974,4274)
INSERT INTO #c VALUES('ORCL',2005,14380,11145,4736,3381)
INSERT INTO #c VALUES('SAP',2009,10672,6980,2588,1748)
INSERT INTO #c VALUES('SAP',2008,11575,7370,2701,1847)
INSERT INTO #c VALUES('SAP',2007,10256,6631,2698,1906)
INSERT INTO #c VALUES('SAP',2006,9393,6064,2578,1871)
INSERT INTO #c VALUES('SAP',2005,8509,5460,2337,1496)
In this example, we will calculate the trend of the revenue (REV) against the year (YE)
SELECT SYM
,ROUND(wct.TREND(wct.fmtTREND(REV,YE,2010)), 0) as [2010 Revnue]
,ROUND(wct.TREND(wct.fmtTREND(REV,YE,2011)), 0) as [2011 Revnue]
,ROUND(wct.TREND(wct.fmtTREND(REV,YE,2012)), 0) as [2012 Revnue]
FROM #c
GROUP BY SYM
This produces the following result.
SYM 2010 Revnue 2011 Revnue 2012 Revnue
----- ---------------------- ---------------------- ----------------------
GOOG 29621 34243 38864
MSFT 68465 72837 77208
ORCL 30016 33030 36044
SAP 12033 12684 13335
YHOO 3853 3458 3063
(5 row(s) affected)
To calculate the net income using the revenue projections from the above query, we could enter the following statement.
SELECT #c.SYM
,ROUND(wct.TREND(wct.fmtTREND(NETINC,REV,[2010 Revenue])), 0) as [2010 Net Income]
,ROUND(wct.TREND(wct.fmtTREND(NETINC,REV,[2011 Revenue])), 0) as [2011 Net Income]
,ROUND(wct.TREND(wct.fmtTREND(NETINC,REV,[2012 Revenue])), 0) as [2012 Net Income]
FROM (
SELECT SYM
,ROUND(wct.TREND(wct.fmtTREND(REV,YE,2010)), 0) as [2010 Revenue]
,ROUND(wct.TREND(wct.fmtTREND(REV,YE,2011)), 0) as [2011 Revenue]
,ROUND(wct.TREND(wct.fmtTREND(REV,YE,2012)), 0) as [2012 Revenue]
FROM #c
GROUP BY SYM) n, #c
WHERE n.sym = #c.sym
GROUP BY #c.SYM
This returns the following results.
SYM 2010 Net Income 2011 Net Income 2012 Net Income
----- ---------------------- ---------------------- ----------------------
GOOG 7107 8176 9246
MSFT 19566 20910 22253
ORCL 7060 7753 8447
SAP 1945 2002 2059
YHOO 807 789 771
(5 row(s) affected)
As the following query demonstrates, this returns a different result than if we had just looked at the net income over time.
SELECT SYM
,ROUND(wct.TREND(wct.fmtTREND(NETINC,YE,2010)), 0) as [2010 Revnue]
,ROUND(wct.TREND(wct.fmtTREND(NETINC,YE,2011)), 0) as [2011 Revnue]
,ROUND(wct.TREND(wct.fmtTREND(NETINC,YE,2012)), 0) as [2012 Revnue]
FROM #c
GROUP BY SYM
This produces the following result.
SYM 2010 Revnue 2011 Revnue 2012 Revnue
----- ---------------------- ---------------------- ----------------------
GOOG 7277 8403 9529
MSFT 19383 20665 21948
ORCL 7029 7712 8394
SAP 1918 1966 2014
YHOO -18 -311 -604
(5 row(s) affected)