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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationSat, 20 Dec 2008 06:50:48 -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/20/t1229781779hlje42ob1u2q2jp.htm/, Retrieved Sat, 18 May 2024 17:10:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=35373, Retrieved Sat, 18 May 2024 17:10:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact207

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 RMPD  [Standard Deviation-Mean Plot] [Non Stationary Ti...] [2008-12-02 11:31:59] [74be16979710d4c4e7c6647856088456]
-    D      [Standard Deviation-Mean Plot] [SMPpaperGeoffrey] [2008-12-20 13:50:48] [9bd6ffcbd02a1b442f4187c5798ef35f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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
142,7
145,11
146,7
148,53
148,99
149,65
151,11
154,82
156,56
157,6
155,24
160,68
163,22
164,55
166,76
159,05
159,82
164,95
162,89
163,55
158,68
157,97
156,59
161,56
162,31
166,26
168,45
163,63
153,2
133,52
123,28

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=35373&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=35373&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35373&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
1133.15252.399792131149548.13
2136.4591666666672.00694004228666.91999999999999
3143.9153.7660215323094210.06
4159.534.7303007784437415.6500000000000
5159.0508333333339.078456475067634.93

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 133.1525 & 2.39979213114954 & 8.13 \tabularnewline
2 & 136.459166666667 & 2.0069400422866 & 6.91999999999999 \tabularnewline
3 & 143.915 & 3.76602153230942 & 10.06 \tabularnewline
4 & 159.53 & 4.73030077844374 & 15.6500000000000 \tabularnewline
5 & 159.050833333333 & 9.0784564750676 & 34.93 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35373&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]133.1525[/C][C]2.39979213114954[/C][C]8.13[/C][/ROW]
[ROW][C]2[/C][C]136.459166666667[/C][C]2.0069400422866[/C][C]6.91999999999999[/C][/ROW]
[ROW][C]3[/C][C]143.915[/C][C]3.76602153230942[/C][C]10.06[/C][/ROW]
[ROW][C]4[/C][C]159.53[/C][C]4.73030077844374[/C][C]15.6500000000000[/C][/ROW]
[ROW][C]5[/C][C]159.050833333333[/C][C]9.0784564750676[/C][C]34.93[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35373&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35373&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
1133.15252.399792131149548.13
2136.4591666666672.00694004228666.91999999999999
3143.9153.7660215323094210.06
4159.534.7303007784437415.6500000000000
5159.0508333333339.078456475067634.93






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-23.1679707411857
beta0.188252906390367
S.D.0.0752229121281322
T-STAT2.50260061814282
p-value0.0875058418632215

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -23.1679707411857 \tabularnewline
beta & 0.188252906390367 \tabularnewline
S.D. & 0.0752229121281322 \tabularnewline
T-STAT & 2.50260061814282 \tabularnewline
p-value & 0.0875058418632215 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35373&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-23.1679707411857[/C][/ROW]
[ROW][C]beta[/C][C]0.188252906390367[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0752229121281322[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.50260061814282[/C][/ROW]
[ROW][C]p-value[/C][C]0.0875058418632215[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35373&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35373&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-23.1679707411857
beta0.188252906390367
S.D.0.0752229121281322
T-STAT2.50260061814282
p-value0.0875058418632215






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-30.1333038231240
beta6.31363539680719
S.D.1.848059573748
T-STAT3.41635923781541
p-value0.041954657793495
Lambda-5.31363539680719

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -30.1333038231240 \tabularnewline
beta & 6.31363539680719 \tabularnewline
S.D. & 1.848059573748 \tabularnewline
T-STAT & 3.41635923781541 \tabularnewline
p-value & 0.041954657793495 \tabularnewline
Lambda & -5.31363539680719 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35373&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-30.1333038231240[/C][/ROW]
[ROW][C]beta[/C][C]6.31363539680719[/C][/ROW]
[ROW][C]S.D.[/C][C]1.848059573748[/C][/ROW]
[ROW][C]T-STAT[/C][C]3.41635923781541[/C][/ROW]
[ROW][C]p-value[/C][C]0.041954657793495[/C][/ROW]
[ROW][C]Lambda[/C][C]-5.31363539680719[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35373&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35373&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-30.1333038231240
beta6.31363539680719
S.D.1.848059573748
T-STAT3.41635923781541
p-value0.041954657793495
Lambda-5.31363539680719


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')