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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_univariatedataseries.wasp
Title produced by softwareUnivariate Data Series
Date of computationTue, 02 Dec 2008 08:16:30 -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/t12282310417yxj70dps2vwm92.htm/, Retrieved Sat, 18 May 2024 05:34:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27934, Retrieved Sat, 18 May 2024 05:34:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact182

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] [Q5] [2008-12-02 15:16:30] [5bd06487453d0eec7a1bf04bf9f25085] [Current]
Feedback Forum
2008-12-04 10:24:07 [72e979bcc364082694890d2eccc1a66f] [reply] 
De student heeft de verkeerde software gebruikt. Er moest gebruik gemaakt worden van de Standard Deviation-Mean Plot analysis.
Hieruit kan je dan afleiden dat we komen tot een lambda van -0,3.
2008-12-07 16:53:58 [Jolien Van Landeghem] [reply] 
Je moest hier een standard deviation mean plot gebruiken. nu vinden we de optimale lambda van -0.3. We hebben gezien op de run sequence plot dat er sprake was van heteroskedasticiteit : de variantie werd groter naarmate de tijd vordert. De gegevens in de standard deviation mean plot bevestigden dit. Ook de p value (6.19…e-11) is kleiner dan 0.05 wat de heteroskedasticiteit bevestigt. We gaan de tijdreeks tot de 0.3e macht verheffenen om deze heteroskedastische trend weg te werken.
Als je dan de data tot de -0.3e macht verheffent en je er opnieuw een run sequence plot of een scatterplot in Excel van zou maken, dan zou je zien dat de heteroskedasticiteit verdwenen is. Het zou wel kunnen dat er nog een trend aanwezig is. Als je met die getransformeerde data een variance reduction matrix maakt , zal je zin dat de variantie niet meer vergroot naarmate de tijd vordert.
2008-12-08 17:39:12 [Hannes Van Hoof] [reply] 
Hier moest de standard deviation mean plot gebruikt worden. hierbij krijg je een als output een tabel waarin je kan aflezen dat lambda -0.3 is.

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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=27934&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=27934&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27934&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=27934&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=27934&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27934&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 ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')