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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 computationTue, 02 Dec 2008 10:57:26 -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/t1228240732p6datd42lsppuys.htm/, Retrieved Fri, 17 May 2024 01:42:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28156, Retrieved Fri, 17 May 2024 01:42:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact175

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]
- RMPD  [Standard Deviation-Mean Plot] [Q5 Randow walk task] [2008-11-29 13:16:45] [6743688719638b0cb1c0a6e0bf433315]
F         [Standard Deviation-Mean Plot] [Q5 ] [2008-11-29 15:18:13] [de72ca3f4fcfd0997c84e1ac92aea119]
F    D      [Standard Deviation-Mean Plot] [Q8 Workshop 4] [2008-12-02 17:47:16] [de72ca3f4fcfd0997c84e1ac92aea119]
F   PD          [Standard Deviation-Mean Plot] [Q8 Workshop 4] [2008-12-02 17:57:26] [56fd94b954e08a6655cb7790b21ee404] [Current]
- RMPD            [(Partial) Autocorrelation Function] [] [2008-12-06 16:30:23] [74be16979710d4c4e7c6647856088456]
- RMPD            [Spectral Analysis] [] [2008-12-06 16:31:48] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2008-12-06 16:34:29 [Ken Wright] [reply] 
goed, maar je maakt hier geen gebruik van de spectraal analyse en ACF
ACF: http://www.freestatistics.org/blog/index.php?v=date/2008/Dec/06/t12285810449t4rncwdz811gpt.htm
Hier zie je een mooie daling, dit duidt op LT, seizoenaliteit is niet aftelezen
spectraal analyse:http://www.freestatistics.org/blog/index.php?v=date/2008/Dec/06/t1228581140qfq6pj9cg6qf1yg.htm
het raw periodogram geeft hoge waarde in het begin: lage frequentie
en geeft een dalend verlood, dit duidt beide op een LT trend. Ook het cumulative periodogram geeft een LT trend weer, omdat deze een steile helling heeft.
Men kan dus besluiten dat er waarschijnlijk alleen niet seizoenaal zal moeten worden gedifferentieerd.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
109,86
108,68
113,38
117,12
116,23
114,75
115,81
115,86
117,80
117,11
116,31
118,38
121,57
121,65
124,20
126,12
128,60
128,16
130,12
135,83
138,05
134,99
132,38
128,94
128,12
127,84
132,43
134,13
134,78
133,13
129,08
134,48
132,86
134,08
134,54
134,51
135,97
136,09
139,14
135,63
136,55
138,83
138,84
135,37
132,22
134,75
135,98
136,06
138,05
139,59
140,58
139,81
140,77
140,96
143,59
142,70
145,11
146,70
148,53
148,99
149,65
151,11
154,82
156,56
157,60
155,24
160,68
163,22
164,55
166,76
159,05
159,82
164,95
162,89

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=28156&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=28156&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28156&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






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
1115.10753.041318867620679.69999999999999
2129.21755.3764945068155916.4800000000000
3132.4983333333332.621615856102816.94
4136.2858333333331.945948977810686.91999999999999
5142.9483333333333.6470008433663910.94
6158.2555.1774309178482417.11

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 115.1075 & 3.04131886762067 & 9.69999999999999 \tabularnewline
2 & 129.2175 & 5.37649450681559 & 16.4800000000000 \tabularnewline
3 & 132.498333333333 & 2.62161585610281 & 6.94 \tabularnewline
4 & 136.285833333333 & 1.94594897781068 & 6.91999999999999 \tabularnewline
5 & 142.948333333333 & 3.64700084336639 & 10.94 \tabularnewline
6 & 158.255 & 5.17743091784824 & 17.11 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28156&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]115.1075[/C][C]3.04131886762067[/C][C]9.69999999999999[/C][/ROW]
[ROW][C]2[/C][C]129.2175[/C][C]5.37649450681559[/C][C]16.4800000000000[/C][/ROW]
[ROW][C]3[/C][C]132.498333333333[/C][C]2.62161585610281[/C][C]6.94[/C][/ROW]
[ROW][C]4[/C][C]136.285833333333[/C][C]1.94594897781068[/C][C]6.91999999999999[/C][/ROW]
[ROW][C]5[/C][C]142.948333333333[/C][C]3.64700084336639[/C][C]10.94[/C][/ROW]
[ROW][C]6[/C][C]158.255[/C][C]5.17743091784824[/C][C]17.11[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28156&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28156&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
1115.10753.041318867620679.69999999999999
2129.21755.3764945068155916.4800000000000
3132.4983333333332.621615856102816.94
4136.2858333333331.945948977810686.91999999999999
5142.9483333333333.6470008433663910.94
6158.2555.1774309178482417.11






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-1.34271557128678
beta0.0366764643761272
S.D.0.0445721771064017
T-STAT0.822855573973287
p-value0.456815762525533

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -1.34271557128678 \tabularnewline
beta & 0.0366764643761272 \tabularnewline
S.D. & 0.0445721771064017 \tabularnewline
T-STAT & 0.822855573973287 \tabularnewline
p-value & 0.456815762525533 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28156&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-1.34271557128678[/C][/ROW]
[ROW][C]beta[/C][C]0.0366764643761272[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0445721771064017[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.822855573973287[/C][/ROW]
[ROW][C]p-value[/C][C]0.456815762525533[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28156&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28156&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)
alpha-1.34271557128678
beta0.0366764643761272
S.D.0.0445721771064017
T-STAT0.822855573973287
p-value0.456815762525533






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-4.70908963018696
beta1.20999361040421
S.D.1.76396840768400
T-STAT0.685949705863989
p-value0.530437025774265
Lambda-0.209993610404211

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -4.70908963018696 \tabularnewline
beta & 1.20999361040421 \tabularnewline
S.D. & 1.76396840768400 \tabularnewline
T-STAT & 0.685949705863989 \tabularnewline
p-value & 0.530437025774265 \tabularnewline
Lambda & -0.209993610404211 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28156&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-4.70908963018696[/C][/ROW]
[ROW][C]beta[/C][C]1.20999361040421[/C][/ROW]
[ROW][C]S.D.[/C][C]1.76396840768400[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.685949705863989[/C][/ROW]
[ROW][C]p-value[/C][C]0.530437025774265[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.209993610404211[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28156&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28156&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)
alpha-4.70908963018696
beta1.20999361040421
S.D.1.76396840768400
T-STAT0.685949705863989
p-value0.530437025774265
Lambda-0.209993610404211


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1.8 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 1.8 ; par6 = 0 ; par7 = 0 ;
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')