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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_variancereduction.wasp
Title produced by softwareVariance Reduction Matrix
Date of computationMon, 08 Dec 2008 05:52:52 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/08/t1228740868y6bytjlzgpclzt2.htm/, Retrieved Thu, 31 Oct 2024 23:06:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=30446, Retrieved Thu, 31 Oct 2024 23:06:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact225
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Law of Averages] [Random Walk Simul...] [2008-11-25 18:05:16] [b98453cac15ba1066b407e146608df68]
F RMPD  [Variance Reduction Matrix] [Q3 Variance Reduc...] [2008-11-30 17:51:02] [a7f04e0e73ce3683561193958d653479]
F   PD      [Variance Reduction Matrix] [Opdracht 8 - Stap...] [2008-12-08 12:52:52] [f1a30f1149cef3ef3ef69d586c6c3c1c] [Current]
-    D        [Variance Reduction Matrix] [VRM - Sparen door...] [2008-12-14 11:50:44] [59aea967d9353ed104ab16378d373ac2]
Feedback Forum
2008-12-15 18:20:09 [Philippe Versluys] [reply
Inderdaad hier zien we in de VRM dat we 1 x gaan differenciëren om de trend eruit te halen (d=1) en 1 x seizoenaal gaan differenciëren om de seizoenaliteit eruit te halen.(D=1)
2008-12-15 19:35:09 [Yara Van Overstraeten] [reply
De variantie van de reeks is het kleinst bij V(Y[t],d=1,D=1). In de 2de kolom van de tabel zien we hier immers het laagste getal (795.483036989776).
Dit wil zeggen dat indien we de reeks 1x differentiëren (door bij 'd' 1 in te vullen) we het lange termijn effect kunnen uitzuiveren.
Er is hier blijkbaar wel degelijk sprake van seizoenaliteit (want D = 1).
Ik had hier misschien inderdaad beter eerst d=0, D=0 kunnen simuleren om zo vervolgens te kijken of het lange termijn effect en seizonaliteit wordt weggewerkt aan de hand van de lambdatransformatie.

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Dataseries X:
235.1
280.7
264.6
240.7
201.4
240.8
241.1
223.8
206.1
174.7
203.3
220.5
299.5
347.4
338.3
327.7
351.6
396.6
438.8
395.6
363.5
378.8
357
369
464.8
479.1
431.3
366.5
326.3
355.1
331.6
261.3
249
205.5
235.6
240.9
264.9
253.8
232.3
193.8
177
213.2
207.2
180.6
188.6
175.4
199
179.6
225.8
234
200.2
183.6
178.2
203.2
208.5
191.8
172.8
148
159.4
154.5
213.2
196.4
182.8
176.4
153.6
173.2
171
151.2
161.9
157.2
201.7
236.4
356.1
398.3
403.7
384.6
365.8
368.1
367.9
347
343.3
292.9
311.5
300.9
366.9
356.9
329.7
316.2
269
289.3
266.2
253.6
233.8
228.4
253.6
260.1
306.6
309.2
309.5
271
279.9
317.9
298.4
246.7
227.3
209.1
259.9
266
320.6
308.5
282.2
262.7
263.5
313.1
284.3
252.6
250.3
246.5
312.7
333.2
446.4
511.6
515.5
506.4
483.2
522.3
509.8
460.7
405.8
375
378.5
406.8
467.8
469.8
429.8
355.8
332.7
378
360.5
334.7
319.5
323.1
363.6
352.1
411.9
388.6
416.4
360.7
338
417.2
388.4
371.1
331.5
353.7
396.7
447
533.5
565.4
542.3
488.7
467.1
531.3
496.1
444
403.4
386.3
394.1
404.1
462.1
448.1
432.3
386.3
395.2
421.9
382.9
384.2
345.5
323.4
372.6
376
462.7
487
444.2
399.3
394.9
455.4
414
375.5
347
339.4
385.8
378.8
451.8
446.1
422.5
383.1
352.8
445.3
367.5
355.1
326.2
319.8
331.8
340.9
394.1
417.2
369.9
349.2
321.4
405.7
342.9
316.5
284.2
270.9
288.8
278.8
324.4
310.9
299
273
279.3
359.2
305
282.1
250.3
246.5
257.9
266.5
315.9
318.4
295.4
266.4
245.8
362.8
324.9
294.2
289.5
295.2
290.3
272
307.4
328.7
292.9
249.1
230.4
361.5
321.7
277.2
260.7
251
257.6
241.8
287.5
292.3
274.7
254.2
230
339
318.2
287
295.8
284
271
262.7
340.6
379.4
373.3
355.2
338.4
466.9
451
422
429.2
425.9
460.7
463.6
541.4
544.2
517.5
469.4
439.4
549
533
506.1
484
457
481.5
469.5
544.7
541.2
521.5
469.7
434.4
542.6
517.3
485.7
465.8
447
426.6
411.6
467.5
484.5
451.2
417.4
379.9
484.7
455
420.8
416.5
376.3
405.6
405.8
500.8
514
475.5
430.1
414.4
538
526
488.5
520.2
504.4
568.5
610.6
818
830.9
835.9
782
762.3
856.9
820.9
769.6
752.2
724.4
723.1
719.5
817.4
803.3
752.5
689
630.4
765.5
757.7
732.2
702.6
683.3
709.5
702.2
784.8
810.9
755.6
656.8
615.1
745.3
694.1
675.7
643.7
622.1
634.6
588
689.7
673.9
647.9
568.8
545.7
632.6
643.8
593.1
579.7
546
562.9
572.5




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 0 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30446&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]0 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30446&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30446&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Variance Reduction Matrix
V(Y[t],d=0,D=0)24040.7319917109Range708.9Trim Var.14891.1423067379
V(Y[t],d=1,D=0)1855.27831616522Range306.2Trim Var.1019.21726473461
V(Y[t],d=2,D=0)3601.51571083278Range388.2Trim Var.1764.07169175190
V(Y[t],d=3,D=0)10155.4683153647Range595.5Trim Var.5250.86267655406
V(Y[t],d=0,D=1)10061.5318845559Range585.7Trim Var.5798.12009737033
V(Y[t],d=1,D=1)795.483036989776Range221.9Trim Var.451.063415764475
V(Y[t],d=2,D=1)1251.20020977106Range223.4Trim Var.751.938251968809
V(Y[t],d=3,D=1)3933.17493248985Range389.7Trim Var.2351.74535475078
V(Y[t],d=0,D=2)23022.65043915Range819Trim Var.13637.4877562041
V(Y[t],d=1,D=2)2352.87163598807Range333.6Trim Var.1332.90434353283
V(Y[t],d=2,D=2)3506.43060400436Range407Trim Var.2059.39114521349
V(Y[t],d=3,D=2)10920.6579647792Range659.1Trim Var.6490.07402051023

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 24040.7319917109 & Range & 708.9 & Trim Var. & 14891.1423067379 \tabularnewline
V(Y[t],d=1,D=0) & 1855.27831616522 & Range & 306.2 & Trim Var. & 1019.21726473461 \tabularnewline
V(Y[t],d=2,D=0) & 3601.51571083278 & Range & 388.2 & Trim Var. & 1764.07169175190 \tabularnewline
V(Y[t],d=3,D=0) & 10155.4683153647 & Range & 595.5 & Trim Var. & 5250.86267655406 \tabularnewline
V(Y[t],d=0,D=1) & 10061.5318845559 & Range & 585.7 & Trim Var. & 5798.12009737033 \tabularnewline
V(Y[t],d=1,D=1) & 795.483036989776 & Range & 221.9 & Trim Var. & 451.063415764475 \tabularnewline
V(Y[t],d=2,D=1) & 1251.20020977106 & Range & 223.4 & Trim Var. & 751.938251968809 \tabularnewline
V(Y[t],d=3,D=1) & 3933.17493248985 & Range & 389.7 & Trim Var. & 2351.74535475078 \tabularnewline
V(Y[t],d=0,D=2) & 23022.65043915 & Range & 819 & Trim Var. & 13637.4877562041 \tabularnewline
V(Y[t],d=1,D=2) & 2352.87163598807 & Range & 333.6 & Trim Var. & 1332.90434353283 \tabularnewline
V(Y[t],d=2,D=2) & 3506.43060400436 & Range & 407 & Trim Var. & 2059.39114521349 \tabularnewline
V(Y[t],d=3,D=2) & 10920.6579647792 & Range & 659.1 & Trim Var. & 6490.07402051023 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30446&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]24040.7319917109[/C][C]Range[/C][C]708.9[/C][C]Trim Var.[/C][C]14891.1423067379[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]1855.27831616522[/C][C]Range[/C][C]306.2[/C][C]Trim Var.[/C][C]1019.21726473461[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]3601.51571083278[/C][C]Range[/C][C]388.2[/C][C]Trim Var.[/C][C]1764.07169175190[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]10155.4683153647[/C][C]Range[/C][C]595.5[/C][C]Trim Var.[/C][C]5250.86267655406[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]10061.5318845559[/C][C]Range[/C][C]585.7[/C][C]Trim Var.[/C][C]5798.12009737033[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]795.483036989776[/C][C]Range[/C][C]221.9[/C][C]Trim Var.[/C][C]451.063415764475[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]1251.20020977106[/C][C]Range[/C][C]223.4[/C][C]Trim Var.[/C][C]751.938251968809[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]3933.17493248985[/C][C]Range[/C][C]389.7[/C][C]Trim Var.[/C][C]2351.74535475078[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]23022.65043915[/C][C]Range[/C][C]819[/C][C]Trim Var.[/C][C]13637.4877562041[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]2352.87163598807[/C][C]Range[/C][C]333.6[/C][C]Trim Var.[/C][C]1332.90434353283[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]3506.43060400436[/C][C]Range[/C][C]407[/C][C]Trim Var.[/C][C]2059.39114521349[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]10920.6579647792[/C][C]Range[/C][C]659.1[/C][C]Trim Var.[/C][C]6490.07402051023[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30446&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30446&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Variance Reduction Matrix
V(Y[t],d=0,D=0)24040.7319917109Range708.9Trim Var.14891.1423067379
V(Y[t],d=1,D=0)1855.27831616522Range306.2Trim Var.1019.21726473461
V(Y[t],d=2,D=0)3601.51571083278Range388.2Trim Var.1764.07169175190
V(Y[t],d=3,D=0)10155.4683153647Range595.5Trim Var.5250.86267655406
V(Y[t],d=0,D=1)10061.5318845559Range585.7Trim Var.5798.12009737033
V(Y[t],d=1,D=1)795.483036989776Range221.9Trim Var.451.063415764475
V(Y[t],d=2,D=1)1251.20020977106Range223.4Trim Var.751.938251968809
V(Y[t],d=3,D=1)3933.17493248985Range389.7Trim Var.2351.74535475078
V(Y[t],d=0,D=2)23022.65043915Range819Trim Var.13637.4877562041
V(Y[t],d=1,D=2)2352.87163598807Range333.6Trim Var.1332.90434353283
V(Y[t],d=2,D=2)3506.43060400436Range407Trim Var.2059.39114521349
V(Y[t],d=3,D=2)10920.6579647792Range659.1Trim Var.6490.07402051023



Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
n <- length(x)
sx <- sort(x)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Reduction Matrix',6,TRUE)
a<-table.row.end(a)
for (bigd in 0:2) {
for (smalld in 0:3) {
mylabel <- 'V(Y[t],d='
mylabel <- paste(mylabel,as.character(smalld),sep='')
mylabel <- paste(mylabel,',D=',sep='')
mylabel <- paste(mylabel,as.character(bigd),sep='')
mylabel <- paste(mylabel,')',sep='')
a<-table.row.start(a)
a<-table.element(a,mylabel,header=TRUE)
myx <- x
if (smalld > 0) myx <- diff(x,lag=1,differences=smalld)
if (bigd > 0) myx <- diff(myx,lag=par1,differences=bigd)
a<-table.element(a,var(myx))
a<-table.element(a,'Range',header=TRUE)
a<-table.element(a,max(myx)-min(myx))
a<-table.element(a,'Trim Var.',header=TRUE)
smyx <- sort(myx)
sn <- length(smyx)
a<-table.element(a,var(smyx[smyx>quantile(smyx,0.05) & smyxa<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable.tab')