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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationSun, 21 Dec 2008 09:36:28 -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/21/t1229878751iioer0ukhpevvyl.htm/, Retrieved Tue, 28 May 2024 01:48:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=35686, Retrieved Tue, 28 May 2024 01:48:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact159

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Standard Deviation-Mean Plot] [Paper ] [2008-12-21 16:36:28] [3452c99afdd85d4fde81272403cd85da] [Current]
Feedback Forum
2009-01-07 19:13:50 [Aurélie Van Impe] [reply] 
Je begint zo ineens met testen uit te voeren, zonder eerst te zeggen wat je gaat doen. Je gaat lambda zoeken heb ik begrepen, maar je had erbij kunnen vermelden waarom je dat doet. Je had kunnen zeggen dat je een poging deed om de spreiding van de voorspellingsfouten constant te krijgen, en dat dit een onderdeel is van het proces om je tijdreeks stationair te maken. Dit heb je ook niet vermeld. Ook heb je niet uitgelegd waarom het nodig is om je tijdreeksen stationair te maken, je zei immers dat je een verband ging onderzoeken tussen de tijdreeksen. Dat heeft bij mijn weten niets te maken met het stationair maken van je tijdreeksen. Je had kunnen zeggen dat je de tijdreeksen stationair wil maken omdat je een stationaire tijdreeks nodig hebt om een voorspelling van de gegevens te kunnen maken. Maar hier zeg je niets over, over voorspellingen...
Ook zeg je dat je de nulhypothese kan verwerpen, maar je zegt niet wat de nulhypothese is. Dit kan je ook niet afleiden uit de tabellen in je blog.

Je had beter gezegd dat lambda niet significant is, omdat ze om te beginnen al buiten het interval [-2,2] komt. Bijgevolg mag je deze waarde laten staan zoals ze default is ingesteld in de software, namelijk op 1. Een andere reden is dat de p-waarde groter is dan 0.05. Ze is dus niet significant. Je had ook naar de grafiek kunnen kijken. Daar zie je dat de bollen niet op een lijn liggen, en dat een eventuele outlier een enorme wijziging in de helling van de best beantwoordende diagonaal teweeg zou kunnen brengen. En nu ik erop let is lambda helemaal niet 3.7782... zoals je beweert, maar 0.61. Ik weet dus niet vanwaar je dat getal haalt. Mijn opmerking dat ze buiten het toegelaten interval ligt is dus onterecht, maar ze is nog steeds niet significant.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
547344
554788
562325
560854
555332
543599
536662
542722
593530
610763
612613
611324
594167
595454
590865
589379
584428
573100
567456
569028
620735
628884
628232
612117
595404
597141
593408
590072
579799
574205
572775
572942
619567
625809
619916
587625
565742
557274
560576
548854
531673
525919
511038
498662
555362
564591
541657
527070
509846
514258
516922
507561
492622
490243
469357
477580
528379
533590
517945
506174
501866

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=35686&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=35686&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35686&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






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
1569321.33333333329214.295265824775951
2596153.7521879.810744257961428
3594055.2518811.974378643253034
4540701.521914.672455188167080
5505373.08333333319499.178572248264233

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 569321.333333333 & 29214.2952658247 & 75951 \tabularnewline
2 & 596153.75 & 21879.8107442579 & 61428 \tabularnewline
3 & 594055.25 & 18811.9743786432 & 53034 \tabularnewline
4 & 540701.5 & 21914.6724551881 & 67080 \tabularnewline
5 & 505373.083333333 & 19499.1785722482 & 64233 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35686&T=1

[TABLE]
[ROW][C]Standard Deviation-Mean Plot[/C][/ROW]
[ROW][C]Section[/C][C]Mean[/C][C]Standard Deviation[/C][C]Range[/C][/ROW]
[ROW][C]1[/C][C]569321.333333333[/C][C]29214.2952658247[/C][C]75951[/C][/ROW]
[ROW][C]2[/C][C]596153.75[/C][C]21879.8107442579[/C][C]61428[/C][/ROW]
[ROW][C]3[/C][C]594055.25[/C][C]18811.9743786432[/C][C]53034[/C][/ROW]
[ROW][C]4[/C][C]540701.5[/C][C]21914.6724551881[/C][C]67080[/C][/ROW]
[ROW][C]5[/C][C]505373.083333333[/C][C]19499.1785722482[/C][C]64233[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35686&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35686&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:

Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
1569321.33333333329214.295265824775951
2596153.7521879.810744257961428
3594055.2518811.974378643253034
4540701.521914.672455188167080
5505373.08333333319499.178572248264233






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha13604.6331851476
beta0.0154322389561055
S.D.0.0613804458574647
T-STAT0.251419466582918
p-value0.817728153107057

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 13604.6331851476 \tabularnewline
beta & 0.0154322389561055 \tabularnewline
S.D. & 0.0613804458574647 \tabularnewline
T-STAT & 0.251419466582918 \tabularnewline
p-value & 0.817728153107057 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35686&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]13604.6331851476[/C][/ROW]
[ROW][C]beta[/C][C]0.0154322389561055[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0613804458574647[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.251419466582918[/C][/ROW]
[ROW][C]p-value[/C][C]0.817728153107057[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35686&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35686&T=2

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


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

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha13604.6331851476
beta0.0154322389561055
S.D.0.0613804458574647
T-STAT0.251419466582918
p-value0.817728153107057






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha4.80957289304724
beta0.392015789596324
S.D.1.41387575193912
T-STAT0.277263252487836
p-value0.799585835278174
Lambda0.607984210403676

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 4.80957289304724 \tabularnewline
beta & 0.392015789596324 \tabularnewline
S.D. & 1.41387575193912 \tabularnewline
T-STAT & 0.277263252487836 \tabularnewline
p-value & 0.799585835278174 \tabularnewline
Lambda & 0.607984210403676 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35686&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]4.80957289304724[/C][/ROW]
[ROW][C]beta[/C][C]0.392015789596324[/C][/ROW]
[ROW][C]S.D.[/C][C]1.41387575193912[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.277263252487836[/C][/ROW]
[ROW][C]p-value[/C][C]0.799585835278174[/C][/ROW]
[ROW][C]Lambda[/C][C]0.607984210403676[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35686&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35686&T=3

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


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

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha4.80957289304724
beta0.392015789596324
S.D.1.41387575193912
T-STAT0.277263252487836
p-value0.799585835278174
Lambda0.607984210403676


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
(n <- length(x))
(np <- floor(n / par1))
arr <- array(NA,dim=c(par1,np))
j <- 0
k <- 1
for (i in 1:(np*par1))
{
j = j + 1
arr[j,k] <- x[i]
if (j == par1) {
j = 0
k=k+1
}
}
arr
arr.mean <- array(NA,dim=np)
arr.sd <- array(NA,dim=np)
arr.range <- array(NA,dim=np)
for (j in 1:np)
{
arr.mean[j] <- mean(arr[,j],na.rm=TRUE)
arr.sd[j] <- sd(arr[,j],na.rm=TRUE)
arr.range[j] <- max(arr[,j],na.rm=TRUE) - min(arr[,j],na.rm=TRUE)
}
arr.mean
arr.sd
arr.range
(lm1 <- lm(arr.sd~arr.mean))
(lnlm1 <- lm(log(arr.sd)~log(arr.mean)))
(lm2 <- lm(arr.range~arr.mean))
bitmap(file='test1.png')
plot(arr.mean,arr.sd,main='Standard Deviation-Mean Plot',xlab='mean',ylab='standard deviation')
dev.off()
bitmap(file='test2.png')
plot(arr.mean,arr.range,main='Range-Mean Plot',xlab='mean',ylab='range')
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Standard Deviation-Mean Plot',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Section',header=TRUE)
a<-table.element(a,'Mean',header=TRUE)
a<-table.element(a,'Standard Deviation',header=TRUE)
a<-table.element(a,'Range',header=TRUE)
a<-table.row.end(a)
for (j in 1:np) {
a<-table.row.start(a)
a<-table.element(a,j,header=TRUE)
a<-table.element(a,arr.mean[j])
a<-table.element(a,arr.sd[j] )
a<-table.element(a,arr.range[j] )
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Regression: S.E.(k) = alpha + beta * Mean(k)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,lm1$coefficients[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,lm1$coefficients[[2]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,summary(lm1)$coefficients[2,2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,summary(lm1)$coefficients[2,3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,summary(lm1)$coefficients[2,4])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,lnlm1$coefficients[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,lnlm1$coefficients[[2]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,summary(lnlm1)$coefficients[2,2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,summary(lnlm1)$coefficients[2,3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,summary(lnlm1)$coefficients[2,4])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Lambda',header=TRUE)
a<-table.element(a,1-lnlm1$coefficients[[2]])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')