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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_variancereduction.wasp
Title produced by softwareVariance Reduction Matrix
Date of computationMon, 01 Dec 2008 11:51:21 -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/01/t1228157524w9rtckfzvj5o4el.htm/, Retrieved Sun, 05 May 2024 09:54:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27145, Retrieved Sun, 05 May 2024 09:54:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact197

Tree of Dependent Computations

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 17:50:19] [b98453cac15ba1066b407e146608df68]
F       [Law of Averages] [q1 non stationary...] [2008-12-01 18:42:47] [85134b6edb9973b9193450dd2306c65b]
-         [Law of Averages] [q1 non stationary...] [2008-12-01 18:44:29] [85134b6edb9973b9193450dd2306c65b]
F RMPD        [Variance Reduction Matrix] [q3 non stationary...] [2008-12-01 18:51:21] [4940af498c7c54f3992f17142bd40069] [Current]
Feedback Forum
2008-12-05 10:22:45 [Stijn Van de Velde] [reply] 
Ik mis toch nog enkele zaken in je antwoord.

De variantie van de reeks is het kleinst bij V(Y[t],d=1,D=0). In de 2de kolom van de tabel zien we hier immers het laagste getal.

d: Degree of non-seasonal differencing = 1
D: Degree of seasonal differencing = 0

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 geen sprake van seizoenaliteit (want D = 0).
2008-12-07 15:59:49 [Nathalie Boden] [reply] 
Bij de tabel bv. de eerste kolom = V(Y[t],d=0,D=0). Dit is een symbolische weergave. V(Y[t],d=1,D=0) heeft bijvoorbeeld samen met V(Y[t],d=0,D=1) de kleinste variantie. In de laatste kolom zien we de getrimde variantie.
d = degree of non-seasonal differencing
D = degree of seasonal differencing
Yt = de tijdreeks t

Yt-1 = vertraging van de tijdreeks. Byt = de backshiftoperator (de operator die wordt toegepast op de tijdreeks t)Bv. B²yt (²= de periode met 2 vertragen) BsYt (getal is de vertraging van Yt met s-perioden. We gaan ook differentiëren. Bijvoorbeeld bij een toename. We gaan zien wat de toename is van de tijdreeks met de wijziging van de periode ervoor). We gaan ook de trend modelleren aan de hand van constructiemethoden. D=0 => 0 x seizonaal gedifferentieerd. s = 12 Er wordt een berekening gedaan van de tijdreeks Yt nadat je een aantal keer hebt gedifferentieerd. De varianties geven aan hoe groot de spreiding is van deze reeks. We gaan dan ook het niet verklarende deel zo klein mogelijk maken. De beste oplossingen zijn diegene met de kleinste variantie. Je moet met andere worden 1x differentiëren.

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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 time1 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 1 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27145&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27145&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27145&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 time1 seconds
R Server'George Udny Yule' @ 72.249.76.132






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=27145&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=27145&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27145&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')