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Description of Statistical Computation

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 computationTue, 02 Dec 2008 12:53:36 -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/02/t1228247683avmdlfa2h2pjvwr.htm/, Retrieved Sun, 19 May 2024 12:37:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28296, Retrieved Sun, 19 May 2024 12:37:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact139

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD    [Variance Reduction Matrix] [NonStationaryTime...] [2008-12-02 19:53:36] [ff1f39dba9ec26bf89aa666d9dcb6cc1] [Current]
Feedback Forum
2008-12-05 15:33:38 [Angelique Van de Vijver] [reply] 
De student heeft een fout gemaakt in haar berekening. Bij “seasonal period” moest er 12 staan en niet 1 zoals de student heeft gedaan aangezien het hier om maandcijfers gaat.

Ik heb de juiste berekening gedaan in onderstaand link:
http://www.freestatistics.org/blog/date/2008/Dec/02/t1228236008o9375zw0km9kaig.htm

De student heeft geen uitleg gegeven over de VRM.
In de variantiereductiematrix zien we dat bij d=1; D=1 de variantie het kleinst is (152.689). We zoeken naar de kleinste variantie want hoe kliener de variantie, hoe meer we kunnen verklaren. Dit vormt dus de beste differentiatie om de tijdreeks stationair te maken. Ook als we kijken naar de getrimde variantie is deze het kleinst bij d=1 ;D=1.
We moeten wel opmerken dat de ACF een betrouwbaarder beeld geeft dan deze variantiereductiematrix, maar meestal komen de uitslagen van deze 2 methoden wel overeen.
2008-12-08 19:32:09 [Stef Vermeiren] [reply] 
De berekening van de student is niet correct. De student heeft een verkeerde periode ingevoerd. Deze moet namelijk gelijk zijn aan 12. Het gaat hier immers om maanden.
De student heeft geen uitleg gegeven bij de tabel.
Bij deze methode bekomen we hetzelfde resultaat. De kleinste variantie is gelijk aan 152.69 waarbij d en D gelijk zijn aan 1. We kunnen de tijdreeks dus stationair maken door 1 maal te differentiëren (1 maal voor seizoenaliteit en 1 maal voor lange termijn trend.)

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
112
118
132
129
121
135
148
148
136
119
104
118
115
126
141
135
125
149
170
170
158
133
114
140
145
150
178
163
172
178
199
199
184
162
146
166
171
180
193
181
183
218
230
242
209
191
172
194
196
196
236
235
229
243
264
272
237
211
180
201
204
188
235
227
234
264
302
293
259
229
203
229
242
233
267
269
270
315
364
347
312
274
237
278
284
277
317
313
318
374
413
405
355
306
271
306
315
301
356
348
355
422
465
467
404
347
305
336
340
318
362
348
363
435
491
505
404
359
310
337
360
342
406
396
420
472
548
559
463
407
362
405
417
391
419
461
472
535
622
606
508
461
390
432

Tables (Output of Computation)





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=28296&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=28296&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28296&T=0

As an alternative you can also use a QR Code:  
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)14391.9172008547Range518Trim Var.9847.79429133858
V(Y[t],d=1,D=0)1139.35152171772Range188Trim Var.612.29533808274
V(Y[t],d=2,D=0)1588.47427829388Range228Trim Var.876.603936507937
V(Y[t],d=3,D=0)3719.00577507599Range327Trim Var.2090.46334906897
V(Y[t],d=0,D=1)1139.35152171772Range188Trim Var.612.29533808274
V(Y[t],d=1,D=1)1588.47427829388Range228Trim Var.876.603936507937
V(Y[t],d=2,D=1)3719.00577507599Range327Trim Var.2090.46334906897
V(Y[t],d=3,D=1)10948.9103802672Range543Trim Var.6705.59085714286
V(Y[t],d=0,D=2)1588.47427829388Range228Trim Var.876.603936507937
V(Y[t],d=1,D=2)3719.00577507599Range327Trim Var.2090.46334906897
V(Y[t],d=2,D=2)10948.9103802672Range543Trim Var.6705.59085714286
V(Y[t],d=3,D=2)35462.4106975289Range1018Trim Var.21524.7421935484

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 14391.9172008547 & Range & 518 & Trim Var. & 9847.79429133858 \tabularnewline
V(Y[t],d=1,D=0) & 1139.35152171772 & Range & 188 & Trim Var. & 612.29533808274 \tabularnewline
V(Y[t],d=2,D=0) & 1588.47427829388 & Range & 228 & Trim Var. & 876.603936507937 \tabularnewline
V(Y[t],d=3,D=0) & 3719.00577507599 & Range & 327 & Trim Var. & 2090.46334906897 \tabularnewline
V(Y[t],d=0,D=1) & 1139.35152171772 & Range & 188 & Trim Var. & 612.29533808274 \tabularnewline
V(Y[t],d=1,D=1) & 1588.47427829388 & Range & 228 & Trim Var. & 876.603936507937 \tabularnewline
V(Y[t],d=2,D=1) & 3719.00577507599 & Range & 327 & Trim Var. & 2090.46334906897 \tabularnewline
V(Y[t],d=3,D=1) & 10948.9103802672 & Range & 543 & Trim Var. & 6705.59085714286 \tabularnewline
V(Y[t],d=0,D=2) & 1588.47427829388 & Range & 228 & Trim Var. & 876.603936507937 \tabularnewline
V(Y[t],d=1,D=2) & 3719.00577507599 & Range & 327 & Trim Var. & 2090.46334906897 \tabularnewline
V(Y[t],d=2,D=2) & 10948.9103802672 & Range & 543 & Trim Var. & 6705.59085714286 \tabularnewline
V(Y[t],d=3,D=2) & 35462.4106975289 & Range & 1018 & Trim Var. & 21524.7421935484 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28296&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]14391.9172008547[/C][C]Range[/C][C]518[/C][C]Trim Var.[/C][C]9847.79429133858[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]1139.35152171772[/C][C]Range[/C][C]188[/C][C]Trim Var.[/C][C]612.29533808274[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]1588.47427829388[/C][C]Range[/C][C]228[/C][C]Trim Var.[/C][C]876.603936507937[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]3719.00577507599[/C][C]Range[/C][C]327[/C][C]Trim Var.[/C][C]2090.46334906897[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]1139.35152171772[/C][C]Range[/C][C]188[/C][C]Trim Var.[/C][C]612.29533808274[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]1588.47427829388[/C][C]Range[/C][C]228[/C][C]Trim Var.[/C][C]876.603936507937[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]3719.00577507599[/C][C]Range[/C][C]327[/C][C]Trim Var.[/C][C]2090.46334906897[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]10948.9103802672[/C][C]Range[/C][C]543[/C][C]Trim Var.[/C][C]6705.59085714286[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]1588.47427829388[/C][C]Range[/C][C]228[/C][C]Trim Var.[/C][C]876.603936507937[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]3719.00577507599[/C][C]Range[/C][C]327[/C][C]Trim Var.[/C][C]2090.46334906897[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]10948.9103802672[/C][C]Range[/C][C]543[/C][C]Trim Var.[/C][C]6705.59085714286[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]35462.4106975289[/C][C]Range[/C][C]1018[/C][C]Trim Var.[/C][C]21524.7421935484[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28296&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28296&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Variance Reduction Matrix
V(Y[t],d=0,D=0)14391.9172008547Range518Trim Var.9847.79429133858
V(Y[t],d=1,D=0)1139.35152171772Range188Trim Var.612.29533808274
V(Y[t],d=2,D=0)1588.47427829388Range228Trim Var.876.603936507937
V(Y[t],d=3,D=0)3719.00577507599Range327Trim Var.2090.46334906897
V(Y[t],d=0,D=1)1139.35152171772Range188Trim Var.612.29533808274
V(Y[t],d=1,D=1)1588.47427829388Range228Trim Var.876.603936507937
V(Y[t],d=2,D=1)3719.00577507599Range327Trim Var.2090.46334906897
V(Y[t],d=3,D=1)10948.9103802672Range543Trim Var.6705.59085714286
V(Y[t],d=0,D=2)1588.47427829388Range228Trim Var.876.603936507937
V(Y[t],d=1,D=2)3719.00577507599Range327Trim Var.2090.46334906897
V(Y[t],d=2,D=2)10948.9103802672Range543Trim Var.6705.59085714286
V(Y[t],d=3,D=2)35462.4106975289Range1018Trim Var.21524.7421935484


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ;
Parameters (R input):
par1 = 1 ;
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')