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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 computationFri, 05 Dec 2008 03:14:55 -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/05/t1228472718a5pn1ys93gmm5rz.htm/, Retrieved Thu, 16 May 2024 17:14:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=29137, Retrieved Thu, 16 May 2024 17:14:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact263

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Standard Deviation-Mean Plot] [Paper Hoofdstuk 4...] [2008-12-05 10:14:55] [286e96bd53289970f8e5f25a93fb50b3] [Current]
- RM      [Variance Reduction Matrix] [Paper Hoofdstuk 4...] [2008-12-05 10:50:46] [6fea0e9a9b3b29a63badf2c274e82506]
- RMP     [(Partial) Autocorrelation Function] [Paper Hoofdstuk 4...] [2008-12-05 11:00:50] [6fea0e9a9b3b29a63badf2c274e82506]
- RMP     [(Partial) Autocorrelation Function] [Paper Hoofdstuk 4...] [2008-12-05 11:12:26] [6fea0e9a9b3b29a63badf2c274e82506]
- RMP     [(Partial) Autocorrelation Function] [Paper Hoofdstuk 4...] [2008-12-05 11:12:26] [6fea0e9a9b3b29a63badf2c274e82506]
- RMP     [(Partial) Autocorrelation Function] [Paper Hoofdstuk 4...] [2008-12-05 11:21:04] [6fea0e9a9b3b29a63badf2c274e82506]
-   P       [(Partial) Autocorrelation Function] [Paper Hoofdstuk 4...] [2008-12-05 22:37:51] [6fea0e9a9b3b29a63badf2c274e82506]
-   P       [(Partial) Autocorrelation Function] [Paper Hoofdstuk 4...] [2008-12-05 23:07:43] [6fea0e9a9b3b29a63badf2c274e82506]
- RMP       [Spectral Analysis] [Paper Hoofdstuk 4...] [2008-12-05 23:23:55] [6fea0e9a9b3b29a63badf2c274e82506]
-   P         [Spectral Analysis] [Paper Hoofdstuk 4...] [2008-12-06 11:37:54] [6fea0e9a9b3b29a63badf2c274e82506]
-   P         [Spectral Analysis] [Paper Hoofdstuk 4...] [2008-12-06 11:44:44] [6fea0e9a9b3b29a63badf2c274e82506]
-    D    [Standard Deviation-Mean Plot] [Paper, hoofdstuk ...] [2008-12-06 15:40:41] [79c17183721a40a589db5f9f561947d8]
- RM D    [Variance Reduction Matrix] [Paper, hoofdstuk ...] [2008-12-06 15:48:43] [79c17183721a40a589db5f9f561947d8]
- RMP       [(Partial) Autocorrelation Function] [Paper, hoofdstuk ...] [2008-12-06 16:03:27] [79c17183721a40a589db5f9f561947d8]
- RMP       [(Partial) Autocorrelation Function] [Paper, hoofdstuk ...] [2008-12-06 16:56:09] [79c17183721a40a589db5f9f561947d8]
-   P         [(Partial) Autocorrelation Function] [Paper, hoofdstuk ...] [2008-12-06 17:05:19] [79c17183721a40a589db5f9f561947d8]
- RMP           [Spectral Analysis] [Paper, hoofdstuk ...] [2008-12-07 11:35:08] [79c17183721a40a589db5f9f561947d8]
-   P             [Spectral Analysis] [Paper, hoofdstuk ...] [2008-12-07 11:55:02] [79c17183721a40a589db5f9f561947d8]
-   P               [Spectral Analysis] [Paper, hoofdstuk ...] [2008-12-07 12:00:47] [79c17183721a40a589db5f9f561947d8]
-   P                 [Spectral Analysis] [Paper 4.4 Spectru...] [2008-12-16 14:34:09] [79c17183721a40a589db5f9f561947d8]
-   P                 [Spectral Analysis] [Paper 4.4 Spectru...] [2008-12-16 14:37:47] [79c17183721a40a589db5f9f561947d8]
-   P                 [Spectral Analysis] [Paper 4.4 Spectru...] [2008-12-16 14:39:52] [79c17183721a40a589db5f9f561947d8]
- RMP               [(Partial) Autocorrelation Function] [Paper, hoofdstuk ...] [2008-12-07 12:59:04] [79c17183721a40a589db5f9f561947d8]
-   P                 [(Partial) Autocorrelation Function] [Paper, hoofdstuk ...] [2008-12-09 14:22:25] [79c17183721a40a589db5f9f561947d8]
- RMPD                [ARIMA Backward Selection] [Paper Hoofdstuk 4...] [2008-12-09 14:31:45] [6fea0e9a9b3b29a63badf2c274e82506]
- RMP                 [ARIMA Backward Selection] [Paper Hoofdstuk 4...] [2008-12-09 14:42:05] [79c17183721a40a589db5f9f561947d8]
-                       [ARIMA Backward Selection] [Paper, hoofdstuk ...] [2008-12-10 20:45:33] [79c17183721a40a589db5f9f561947d8]
-   P           [(Partial) Autocorrelation Function] [Paper 4.3 (P)ACF ...] [2008-12-16 14:21:32] [79c17183721a40a589db5f9f561947d8]
-   P             [(Partial) Autocorrelation Function] [Paper 4.3 (P)ACF ...] [2008-12-16 14:24:05] [79c17183721a40a589db5f9f561947d8]
-                 [(Partial) Autocorrelation Function] [Paper 4.3 (P)ACF ...] [2008-12-16 14:28:29] [79c17183721a40a589db5f9f561947d8]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
493.000
481.000
462.000
457.000
442.000
439.000
488.000
521.000
501.000
485.000
464.000
460.000
467.000
460.000
448.000
443.000
436.000
431.000
484.000
510.000
513.000
503.000
471.000
471.000
476.000
475.000
470.000
461.000
455.000
456.000
517.000
525.000
523.000
519.000
509.000
512.000
519.000
517.000
510.000
509.000
501.000
507.000
569.000
580.000
578.000
565.000
547.000
555.000
562.000
561.000
555.000
544.000
537.000
543.000
594.000
611.000
613.000
611.000
594.000
595.000

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=29137&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=29137&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29137&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
1474.41666666666724.570708108837682
2469.7528.178408884689182
3491.528.195744359743470
4538.08333333333330.42265403918479
5576.66666666666729.10586736641876

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 474.416666666667 & 24.5707081088376 & 82 \tabularnewline
2 & 469.75 & 28.1784088846891 & 82 \tabularnewline
3 & 491.5 & 28.1957443597434 & 70 \tabularnewline
4 & 538.083333333333 & 30.422654039184 & 79 \tabularnewline
5 & 576.666666666667 & 29.105867366418 & 76 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29137&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]474.416666666667[/C][C]24.5707081088376[/C][C]82[/C][/ROW]
[ROW][C]2[/C][C]469.75[/C][C]28.1784088846891[/C][C]82[/C][/ROW]
[ROW][C]3[/C][C]491.5[/C][C]28.1957443597434[/C][C]70[/C][/ROW]
[ROW][C]4[/C][C]538.083333333333[/C][C]30.422654039184[/C][C]79[/C][/ROW]
[ROW][C]5[/C][C]576.666666666667[/C][C]29.105867366418[/C][C]76[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29137&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29137&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
1474.41666666666724.570708108837682
2469.7528.178408884689182
3491.528.195744359743470
4538.08333333333330.42265403918479
5576.66666666666729.10586736641876






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha12.8465772581982
beta0.0298933493747614
S.D.0.0211136961903216
T-STAT1.41582739020676
p-value0.251788806077126

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 12.8465772581982 \tabularnewline
beta & 0.0298933493747614 \tabularnewline
S.D. & 0.0211136961903216 \tabularnewline
T-STAT & 1.41582739020676 \tabularnewline
p-value & 0.251788806077126 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29137&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]12.8465772581982[/C][/ROW]
[ROW][C]beta[/C][C]0.0298933493747614[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0211136961903216[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.41582739020676[/C][/ROW]
[ROW][C]p-value[/C][C]0.251788806077126[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29137&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29137&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)
alpha12.8465772581982
beta0.0298933493747614
S.D.0.0211136961903216
T-STAT1.41582739020676
p-value0.251788806077126






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-0.231282051224632
beta0.572000018390146
S.D.0.403919978108336
T-STAT1.41612212663749
p-value0.251710962937155
Lambda0.427999981609854

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -0.231282051224632 \tabularnewline
beta & 0.572000018390146 \tabularnewline
S.D. & 0.403919978108336 \tabularnewline
T-STAT & 1.41612212663749 \tabularnewline
p-value & 0.251710962937155 \tabularnewline
Lambda & 0.427999981609854 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29137&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.231282051224632[/C][/ROW]
[ROW][C]beta[/C][C]0.572000018390146[/C][/ROW]
[ROW][C]S.D.[/C][C]0.403919978108336[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.41612212663749[/C][/ROW]
[ROW][C]p-value[/C][C]0.251710962937155[/C][/ROW]
[ROW][C]Lambda[/C][C]0.427999981609854[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29137&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29137&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-0.231282051224632
beta0.572000018390146
S.D.0.403919978108336
T-STAT1.41612212663749
p-value0.251710962937155
Lambda0.427999981609854


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')