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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_univariatedataseries.wasp
Title produced by softwareUnivariate Data Series
Date of computationMon, 01 Dec 2008 09:43:33 -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/t1228149856cn1958lz7hk42kv.htm/, Retrieved Sun, 05 May 2024 16:54:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26984, Retrieved Sun, 05 May 2024 16:54:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact265

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    D    [Univariate Data Series] [non stationary ti...] [2008-12-01 16:43:33] [fdd9b7950d7c195d4d8aeb0c9bacacc6] [Current]
Feedback Forum
2008-12-05 14:51:51 [Vincent Vanden Poel] [reply] 
Je geeft een goede algemene uitleg maar beantwoord de vraag eigelijk niet. Om dit op te lossen moeten we de Standard Deviation-Mean Plot hanteren. Deze geeft een tabel weer waarin we de optimale lambda waarde te zien krijgen voor de modelvergelijking. In dit geval kunnen we vaststellen dat de beste transformatie berekend wordt met een Lambda waarde van -0.31.

Dit is de correcte module voor het beantwoorden van deze vraag:
http://www.freestatistics.org/blog/index.php?v=date/2008/Dec/02/t12282240305ir28i1uzevf5dx.htm
2008-12-06 16:16:31 [Steven Vanhooreweghe] [reply] 
Je moet eigenlijk een Standard variation mean-plot hanteren. Dan bekom je een tabel waarin staat hoeveel uw lamba is en meteen de oplossing van de vraag.
2008-12-06 16:19:48 [Jonas Janssens] [reply] 
Je geeft geen antwoord op de vraag en gebruikt de verkeerde software. Je moest de Standard Deviation-Mean Plot gebruiken om de lambda transformatie parameter te vinden, die -0.31 moet bedragen om de tijdreeks stationair te maken.
2008-12-07 12:32:37 [006ad2c49b6a7c2ad6ab685cfc1dae56] [reply] 
De grafiek toont een trendmatig verloop, er is sprake van seizoenaliteit en de spreiding is niet constant. Om dit op te lossen moeten we tijdreeks modelleren, proberen stationair te maken. Stationair willen zeggen dat het niveau gelijk (horizontaal) wordt en dat de spreiding gelijk wordt. We halen de trend eruit.

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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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26984&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26984&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26984&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Univariate Dataseries
Name of dataseriesAirline
SourceBox-Jenkins
DescriptionAirline Passengers
Number of observations144

\begin{tabular}{lllllllll}
\hline
Univariate Dataseries \tabularnewline
Name of dataseries & Airline \tabularnewline
Source & Box-Jenkins \tabularnewline
Description & Airline Passengers \tabularnewline
Number of observations & 144 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26984&T=1

[TABLE]
[ROW][C]Univariate Dataseries[/C][/ROW]
[ROW][C]Name of dataseries[/C][C]Airline[/C][/ROW]
[ROW][C]Source[/C][C]Box-Jenkins[/C][/ROW]
[ROW][C]Description[/C][C]Airline Passengers[/C][/ROW]
[ROW][C]Number of observations[/C][C]144[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26984&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26984&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:

Univariate Dataseries
Name of dataseriesAirline
SourceBox-Jenkins
DescriptionAirline Passengers
Number of observations144


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = Airline ; par2 = Box-Jenkins ; par3 = Airline Passengers ;
Parameters (R input):
par1 = Airline ; par2 = Box-Jenkins ; par3 = Airline Passengers ;
R code (references can be found in the software module):
bitmap(file='test1.png')
plot(x,col=2,type='b',main=main,xlab=xlab,ylab=ylab)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Univariate Dataseries',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Name of dataseries',header=TRUE)
a<-table.element(a,par1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Source',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Number of observations',header=TRUE)
a<-table.element(a,length(x))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')