Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_smp.wasp

Title produced by software

Standard Deviation-Mean Plot

Date of computation

Mon, 01 Dec 2008 14:13:37 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/01/t1228166129wz3tz21y04k5lkr.htm/, Retrieved Sun, 05 May 2024 12:52:15 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27411, Retrieved Sun, 05 May 2024 12:52:15 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

217

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 18:40:39] [b98453cac15ba1066b407e146608df68]
F RMPD    [Standard Deviation-Mean Plot] [Non stationary ti...] [2008-12-01 21:13:37] [c5d6d05aee6be5527ac4a30a8c3b8fe5] [Current]
F RM        [Variance Reduction Matrix] [Non stat time ser...] [2008-12-02 17:48:28] [7849b5cbaea5f05923be73656f726e58] 
F RMP       [Spectral Analysis] [Non stat time ser...] [2008-12-02 17:59:13] [7849b5cbaea5f05923be73656f726e58] 
F RMP       [Spectral Analysis] [Non stat time ser...] [2008-12-02 18:04:21] [7849b5cbaea5f05923be73656f726e58] 
F RMP       [Spectral Analysis] [Non stat time ser...] [2008-12-02 18:07:02] [7849b5cbaea5f05923be73656f726e58] 

Feedback Forum

2008-12-06 14:07:12 [Thomas Plasschaert] [reply] 
de seasonal periods moeten op 12 worden gezet, dit wilt zeggen dat we rekenen over 12 maanden tijd.  
de lamba berekenen doen we door de standard deviation plot. deze verdeelt de reeks onder in perioden. in de grafiek zie je punten die de jaren voorstellen.  
x-as = het gemiddelde  
y-as = standard deviation  
 
Door een lambda toe te voegen kan men de tijdreeks transformeren waardoor de spreiding harder naar een diagonaal zal deinen. Een lambda van 1 geeft bijvoorbeeld een perfecte rechte. De bedoeling van deze transformatie is om de tijdreeks stationair proberen te krijgen. Hier voor moet je zien dat de spreiding gelijk loopt door de tijd en hierdoor wordt de trend eruit gehaald.  
de optimale lambda is -0.312592539725757  
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 (6.19â€¦e-11) is kleiner dan 0.05 wat de heteroskedasticiteit bevestigt. We gaan de tijdreeks tot de 0.3e macht vereffenen om deze heteroskedastische trend weg te werken. 
 
2008-12-08 13:23:44 [Katja van Hek] [reply] 
Om een tijdreeks stationair te maken moet je de trend eruit halen en de spreiding doorheen de tijd gelijk maken. Dit doe je door d=1 te stellen en D ook gelijk te stellen aan 1. Hier is het belangrijk dat we lambda vinden. De seizoenale periode zou 12 maanden moeten bedragen. De lambda kun je vinden door gebruik te maken van de standard deviation plot.
2008-12-08 13:24:44 [Katja van Hek] [reply] 
De lambda zou trouwens -0.312592539725757 moeten zijn om een perfect stationaire tijdreeks te krijgen.
2008-12-08 14:36:15 [Jonas Janssens] [reply] 
Je hebt de juiste berekeningen uitgevoerd, maar niet de juiste tabel in je document weergegeven en hiermee geen antwoord gegeven op de vraag. Je moet trachten de meest optimale Lambda transformatieparameter te zoeken om de tijdreeks stationair te maken. Hiervoor moest je in de 'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)'-tabel kijken, waarin Lambda gegeven staat. Deze bedraagt -0.3126. Deze Lambda-waarde heb je dus nodig om de tijdreeks stationair te maken in de volgende vragen.
2008-12-08 19:34:55 [5faab2fc6fb120339944528a32d48a04] [reply] 
De student werkte het goed uit. 
De grafiek vertoont 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 of maw de trend eruit halen. Stationair willen zeggen dat het niveau gelijk (horizontaal) wordt en dat de spreiding gelijk wordt. 
Je had je reeks ook stationair kunnen maken.
2008-12-09 22:23:48 [Gert-Jan Geudens] [reply] 
Goede conclusie. De transformatie is hier zeker aan te raden aangezien de p-waarde zeer klein is. We zullen yt tot de -0.3de macht zetten om een goede transformatie toe te passen. Ook uit de standard-deviation mean plot kunnen we afleiden dat we een zonder enige grote moeite een regressierechte kunnen tekenen doorheen de punten. Transformatie is hier dus zeker en vast mogelijk.

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 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=27411&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=27411&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27411&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	126.666666666667	13.7201466552812	44
2	139.666666666667	19.0708408230201	56
3	170.166666666667	18.4382671899964	54
4	197	22.9663785881568	71
5	225	28.4668866643972	92
6	238.916666666667	34.9244856364370	114
7	284	42.1404577789347	131
8	328.25	47.8617801591207	142
9	368.416666666667	57.8908979081	166
10	381	64.5304720126997	195
11	428.333333333333	69.8300968368398	217
12	476.166666666667	77.7371250179771	232

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 126.666666666667 & 13.7201466552812 & 44 \tabularnewline
2 & 139.666666666667 & 19.0708408230201 & 56 \tabularnewline
3 & 170.166666666667 & 18.4382671899964 & 54 \tabularnewline
4 & 197 & 22.9663785881568 & 71 \tabularnewline
5 & 225 & 28.4668866643972 & 92 \tabularnewline
6 & 238.916666666667 & 34.9244856364370 & 114 \tabularnewline
7 & 284 & 42.1404577789347 & 131 \tabularnewline
8 & 328.25 & 47.8617801591207 & 142 \tabularnewline
9 & 368.416666666667 & 57.8908979081 & 166 \tabularnewline
10 & 381 & 64.5304720126997 & 195 \tabularnewline
11 & 428.333333333333 & 69.8300968368398 & 217 \tabularnewline
12 & 476.166666666667 & 77.7371250179771 & 232 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27411&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]126.666666666667[/C][C]13.7201466552812[/C][C]44[/C][/ROW]
[ROW][C]2[/C][C]139.666666666667[/C][C]19.0708408230201[/C][C]56[/C][/ROW]
[ROW][C]3[/C][C]170.166666666667[/C][C]18.4382671899964[/C][C]54[/C][/ROW]
[ROW][C]4[/C][C]197[/C][C]22.9663785881568[/C][C]71[/C][/ROW]
[ROW][C]5[/C][C]225[/C][C]28.4668866643972[/C][C]92[/C][/ROW]
[ROW][C]6[/C][C]238.916666666667[/C][C]34.9244856364370[/C][C]114[/C][/ROW]
[ROW][C]7[/C][C]284[/C][C]42.1404577789347[/C][C]131[/C][/ROW]
[ROW][C]8[/C][C]328.25[/C][C]47.8617801591207[/C][C]142[/C][/ROW]
[ROW][C]9[/C][C]368.416666666667[/C][C]57.8908979081[/C][C]166[/C][/ROW]
[ROW][C]10[/C][C]381[/C][C]64.5304720126997[/C][C]195[/C][/ROW]
[ROW][C]11[/C][C]428.333333333333[/C][C]69.8300968368398[/C][C]217[/C][/ROW]
[ROW][C]12[/C][C]476.166666666667[/C][C]77.7371250179771[/C][C]232[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27411&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27411&T=1

As an alternative you can also use a QR Code:

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

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	126.666666666667	13.7201466552812	44
2	139.666666666667	19.0708408230201	56
3	170.166666666667	18.4382671899964	54
4	197	22.9663785881568	71
5	225	28.4668866643972	92
6	238.916666666667	34.9244856364370	114
7	284	42.1404577789347	131
8	328.25	47.8617801591207	142
9	368.416666666667	57.8908979081	166
10	381	64.5304720126997	195
11	428.333333333333	69.8300968368398	217
12	476.166666666667	77.7371250179771	232

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	-11.4032541425579
beta	0.188613398899484
S.D.	0.00657733180244678
T-STAT	28.6762785525460
p-value	6.19171705602778e-11

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -11.4032541425579 \tabularnewline
beta & 0.188613398899484 \tabularnewline
S.D. & 0.00657733180244678 \tabularnewline
T-STAT & 28.6762785525460 \tabularnewline
p-value & 6.19171705602778e-11 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27411&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-11.4032541425579[/C][/ROW]
[ROW][C]beta[/C][C]0.188613398899484[/C][/ROW]
[ROW][C]S.D.[/C][C]0.00657733180244678[/C][/ROW]
[ROW][C]T-STAT[/C][C]28.6762785525460[/C][/ROW]
[ROW][C]p-value[/C][C]6.19171705602778e-11[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27411&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27411&T=2

As an alternative you can also use a QR Code:

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

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	-11.4032541425579
beta	0.188613398899484
S.D.	0.00657733180244678
T-STAT	28.6762785525460
p-value	6.19171705602778e-11

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	-3.70703989322048
beta	1.31259253972576
S.D.	0.0574958902763332
T-STAT	22.8293280340083
p-value	5.8658934502011e-10
Lambda	-0.312592539725757

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -3.70703989322048 \tabularnewline
beta & 1.31259253972576 \tabularnewline
S.D. & 0.0574958902763332 \tabularnewline
T-STAT & 22.8293280340083 \tabularnewline
p-value & 5.8658934502011e-10 \tabularnewline
Lambda & -0.312592539725757 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27411&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-3.70703989322048[/C][/ROW]
[ROW][C]beta[/C][C]1.31259253972576[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0574958902763332[/C][/ROW]
[ROW][C]T-STAT[/C][C]22.8293280340083[/C][/ROW]
[ROW][C]p-value[/C][C]5.8658934502011e-10[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.312592539725757[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27411&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27411&T=3

As an alternative you can also use a QR Code:

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

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	-3.70703989322048
beta	1.31259253972576
S.D.	0.0574958902763332
T-STAT	22.8293280340083
p-value	5.8658934502011e-10
Lambda	-0.312592539725757

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code