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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 computationSat, 19 Dec 2009 11:05:59 -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/2009/Dec/19/t1261246016f6jyaaxylspsv43.htm/, Retrieved Sat, 04 May 2024 05:13:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=69729, Retrieved Sat, 04 May 2024 05:13:32 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Pearson Correlation] [Correlation: inve...] [2008-12-16 19:18:46] [5161246d1ccc1b670cc664d03050f084]
- RMPD  [Univariate Data Series] [] [2008-12-17 14:56:45] [b98453cac15ba1066b407e146608df68]
- RMP     [Standard Deviation-Mean Plot] [] [2008-12-17 19:22:22] [b98453cac15ba1066b407e146608df68]
-  MPD      [Standard Deviation-Mean Plot] [Standard Deviatio...] [2009-12-15 19:47:25] [1eab65e90adf64584b8e6f0da23ff414]
-   PD          [Standard Deviation-Mean Plot] [Standard Deviatio...] [2009-12-19 18:05:59] [0f1f1142419956a95ff6f880845f2408] [Current]
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Dataseries X:
100,5
106,29
101,09
104,53
122,74
109,84
101,99
125,12
103,5
102,8
118,72
119,01
118,61
120,43
111,83
116,79
131,71
120,57
117,83
130,8
107,46
112,09
129,47
119,72
134,81
135,8
129,27
126,94
153,45
121,86
133,47
135,34
117,1
120,65
132,49
137,6
138,69
125,53
133,09
129,08
145,94
129,07
139,69
142,09
137,29
127,03
137,25
156,87
150,89
139,14
158,3
149
158,36
168,06
153,38
173,86
162,47
145,17
168,89
166,64
140,07
128,84
123,41
120,3
129,67
118,1
113,91
131,09
119,15
122,3




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=69729&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=69729&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69729&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 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
1109.67759.1377897914499724.62
2119.7758333333337.6689687090642224.25
3131.5659.590682504863336.35
4136.8016666666678.9558338866942331.34
5157.84666666666710.613491699747434.72

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 109.6775 & 9.13778979144997 & 24.62 \tabularnewline
2 & 119.775833333333 & 7.66896870906422 & 24.25 \tabularnewline
3 & 131.565 & 9.5906825048633 & 36.35 \tabularnewline
4 & 136.801666666667 & 8.95583388669423 & 31.34 \tabularnewline
5 & 157.846666666667 & 10.6134916997474 & 34.72 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69729&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]109.6775[/C][C]9.13778979144997[/C][C]24.62[/C][/ROW]
[ROW][C]2[/C][C]119.775833333333[/C][C]7.66896870906422[/C][C]24.25[/C][/ROW]
[ROW][C]3[/C][C]131.565[/C][C]9.5906825048633[/C][C]36.35[/C][/ROW]
[ROW][C]4[/C][C]136.801666666667[/C][C]8.95583388669423[/C][C]31.34[/C][/ROW]
[ROW][C]5[/C][C]157.846666666667[/C][C]10.6134916997474[/C][C]34.72[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69729&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69729&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
SectionMeanStandard DeviationRange
1109.67759.1377897914499724.62
2119.7758333333337.6689687090642224.25
3131.5659.590682504863336.35
4136.8016666666678.9558338866942331.34
5157.84666666666710.613491699747434.72







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha3.76568739640174
beta0.0413904366189278
S.D.0.0238070353088657
T-STAT1.73858004921403
p-value0.180494705892607

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 3.76568739640174 \tabularnewline
beta & 0.0413904366189278 \tabularnewline
S.D. & 0.0238070353088657 \tabularnewline
T-STAT & 1.73858004921403 \tabularnewline
p-value & 0.180494705892607 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69729&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]3.76568739640174[/C][/ROW]
[ROW][C]beta[/C][C]0.0413904366189278[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0238070353088657[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.73858004921403[/C][/ROW]
[ROW][C]p-value[/C][C]0.180494705892607[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69729&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69729&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)
alpha3.76568739640174
beta0.0413904366189278
S.D.0.0238070353088657
T-STAT1.73858004921403
p-value0.180494705892607







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-0.538550307102251
beta0.565159395991471
S.D.0.372148683729965
T-STAT1.51863870732258
p-value0.226157276248512
Lambda0.434840604008529

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -0.538550307102251 \tabularnewline
beta & 0.565159395991471 \tabularnewline
S.D. & 0.372148683729965 \tabularnewline
T-STAT & 1.51863870732258 \tabularnewline
p-value & 0.226157276248512 \tabularnewline
Lambda & 0.434840604008529 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69729&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.538550307102251[/C][/ROW]
[ROW][C]beta[/C][C]0.565159395991471[/C][/ROW]
[ROW][C]S.D.[/C][C]0.372148683729965[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.51863870732258[/C][/ROW]
[ROW][C]p-value[/C][C]0.226157276248512[/C][/ROW]
[ROW][C]Lambda[/C][C]0.434840604008529[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69729&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69729&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-0.538550307102251
beta0.565159395991471
S.D.0.372148683729965
T-STAT1.51863870732258
p-value0.226157276248512
Lambda0.434840604008529



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')