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Author*The author of this computation has been verified*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationSun, 14 Dec 2014 17:56:45 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/14/t1418579903dgc75r30yz5r96m.htm/, Retrieved Thu, 31 Oct 2024 23:35:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267782, Retrieved Thu, 31 Oct 2024 23:35:57 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact110
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Standard Deviation-Mean Plot] [] [2014-12-14 17:56:45] [0a6fc2c777821367d2239c664b701a36] [Current]
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Dataseries X:
1.894
1.757
3.582
5.321
5.561
5.907
4.944
4.966
3.258
1.964
1.743
1.262
2.086
1.793
3.548
5.672
6.084
4.914
4.990
5.139
3.218
2.179
2.238
1.442
2.205
2.025
3.531
4.977
7.998
4.880
5.231
5.202
3.303
2.683
2.202
1.376
2.422
1.997
3.163
5.964
5.657
6.415
6.208
4.500
2.939
2.702
2.090
1.504
2.549
1.931
3.013
6.204
5.788
5.611
5.594
4.647
3.490
2.487
1.992
1.507
2.306
2.002
3.075
5.331
5.589
5.813
4.876
4.665
3.601
2.192
2.111
1.580
2.288
1.993
3.228
5.000
5.480
5.770
4.962
4.685
3.607
2.222
2.467
1.594
2.228
1.910
3.157
4.809
6.249
4.607
4.975
4.784
3.028
2.461
2.218
1.351
2.070
1.887
3.024
4.596
6.398
4.459
5.382
4.359
2.687
2.249
2.154
1.169
2.429
1.762
2.846
5.627
5.749
4.502
5.720
4.403
2.867
2.635
2.059
1.511
2.359
1.741
2.917
6.249
5.760
6.250
5.134
4.831
3.695
2.462
2.146
1.579




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267782&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267782&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267782&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'Gertrude Mary Cox' @ cox.wessa.net







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
13.513251.751029781429934.645
23.608583333333331.671338465168424.642
33.801083333333331.904047672165486.622
43.796751.832931339536364.911
53.734416666666671.729940222736734.697
63.595083333333331.578135059223874.233
73.6081.504044124110974.176
83.481416666666671.541658814885824.898
93.36951.621986296321445.229
103.509166666666671.598460954869534.238
113.760251.789635164699024.671

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 3.51325 & 1.75102978142993 & 4.645 \tabularnewline
2 & 3.60858333333333 & 1.67133846516842 & 4.642 \tabularnewline
3 & 3.80108333333333 & 1.90404767216548 & 6.622 \tabularnewline
4 & 3.79675 & 1.83293133953636 & 4.911 \tabularnewline
5 & 3.73441666666667 & 1.72994022273673 & 4.697 \tabularnewline
6 & 3.59508333333333 & 1.57813505922387 & 4.233 \tabularnewline
7 & 3.608 & 1.50404412411097 & 4.176 \tabularnewline
8 & 3.48141666666667 & 1.54165881488582 & 4.898 \tabularnewline
9 & 3.3695 & 1.62198629632144 & 5.229 \tabularnewline
10 & 3.50916666666667 & 1.59846095486953 & 4.238 \tabularnewline
11 & 3.76025 & 1.78963516469902 & 4.671 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267782&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]3.51325[/C][C]1.75102978142993[/C][C]4.645[/C][/ROW]
[ROW][C]2[/C][C]3.60858333333333[/C][C]1.67133846516842[/C][C]4.642[/C][/ROW]
[ROW][C]3[/C][C]3.80108333333333[/C][C]1.90404767216548[/C][C]6.622[/C][/ROW]
[ROW][C]4[/C][C]3.79675[/C][C]1.83293133953636[/C][C]4.911[/C][/ROW]
[ROW][C]5[/C][C]3.73441666666667[/C][C]1.72994022273673[/C][C]4.697[/C][/ROW]
[ROW][C]6[/C][C]3.59508333333333[/C][C]1.57813505922387[/C][C]4.233[/C][/ROW]
[ROW][C]7[/C][C]3.608[/C][C]1.50404412411097[/C][C]4.176[/C][/ROW]
[ROW][C]8[/C][C]3.48141666666667[/C][C]1.54165881488582[/C][C]4.898[/C][/ROW]
[ROW][C]9[/C][C]3.3695[/C][C]1.62198629632144[/C][C]5.229[/C][/ROW]
[ROW][C]10[/C][C]3.50916666666667[/C][C]1.59846095486953[/C][C]4.238[/C][/ROW]
[ROW][C]11[/C][C]3.76025[/C][C]1.78963516469902[/C][C]4.671[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267782&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267782&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
13.513251.751029781429934.645
23.608583333333331.671338465168424.642
33.801083333333331.904047672165486.622
43.796751.832931339536364.911
53.734416666666671.729940222736734.697
63.595083333333331.578135059223874.233
73.6081.504044124110974.176
83.481416666666671.541658814885824.898
93.36951.621986296321445.229
103.509166666666671.598460954869534.238
113.760251.789635164699024.671







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-0.608058346526129
beta0.633821876863427
S.D.0.211673404325907
T-STAT2.99433874974464
p-value0.0150943113530639

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -0.608058346526129 \tabularnewline
beta & 0.633821876863427 \tabularnewline
S.D. & 0.211673404325907 \tabularnewline
T-STAT & 2.99433874974464 \tabularnewline
p-value & 0.0150943113530639 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267782&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.608058346526129[/C][/ROW]
[ROW][C]beta[/C][C]0.633821876863427[/C][/ROW]
[ROW][C]S.D.[/C][C]0.211673404325907[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.99433874974464[/C][/ROW]
[ROW][C]p-value[/C][C]0.0150943113530639[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267782&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267782&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-0.608058346526129
beta0.633821876863427
S.D.0.211673404325907
T-STAT2.99433874974464
p-value0.0150943113530639







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-1.17216468481041
beta1.31602228605555
S.D.0.462049016522975
T-STAT2.84823089974073
p-value0.0191443519178134
Lambda-0.316022286055552

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -1.17216468481041 \tabularnewline
beta & 1.31602228605555 \tabularnewline
S.D. & 0.462049016522975 \tabularnewline
T-STAT & 2.84823089974073 \tabularnewline
p-value & 0.0191443519178134 \tabularnewline
Lambda & -0.316022286055552 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267782&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-1.17216468481041[/C][/ROW]
[ROW][C]beta[/C][C]1.31602228605555[/C][/ROW]
[ROW][C]S.D.[/C][C]0.462049016522975[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.84823089974073[/C][/ROW]
[ROW][C]p-value[/C][C]0.0191443519178134[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.316022286055552[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267782&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267782&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-1.17216468481041
beta1.31602228605555
S.D.0.462049016522975
T-STAT2.84823089974073
p-value0.0191443519178134
Lambda-0.316022286055552



Parameters (Session):
par1 = Default ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
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')