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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 computationTue, 02 Dec 2008 11:17:16 -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/t1228241865dtbsa262y3bhrhv.htm/, Retrieved Sun, 19 May 2024 10:51:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28199, Retrieved Sun, 19 May 2024 10:51:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact215

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Variance Reduction Matrix] [] [2008-12-02 14:43:56] [b53e8d20687f12ca59f39c9b7c3a629a]
F         [Variance Reduction Matrix] [Q6] [2008-12-02 18:17:16] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum
2008-12-04 18:52:16 [c97d2ae59c98cf77a04815c1edffab5a] [reply] 
deze VRM is fout gemaakt, want de seasonal period=1 en zou ingesteld moeten zijn op 12.
hierdoor krijgen we een andere oplossing!
de juiste link: http://www.freestatistics.org/blog/index.php?v=date/2008/Dec/02/t1228250726rfabernaogew35c.htm

=>Kleinste variantie zoeken
=>Geeft hier hetzelfde effect als de autocorrelation fuction
<=>Soms is dit anders: variantie is gevoelig voor outliers, waardoor deze vertekend kan zijn
=>Als je moet kiezen: autocorrelation gebruiken
=>Laatste kolom is getrimde variantie: laat outliers weg => veiliger

conclusie:
Aangezien 152 de kleinste variantie is (of 73 in geval van getrimde variantie), moeten we differentiëren dmv:
-d =1
-D=1

Post a new message

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'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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28199&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28199&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28199&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'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=28199&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=28199&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28199&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')