FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationMon, 01 Dec 2008 13:41:45 -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/01/t1228164139zlsccr36q5sbi34.htm/, Retrieved Sun, 05 May 2024 14:50:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27371, Retrieved Sun, 05 May 2024 14:50:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact258

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  [Variance Reduction Matrix] [Non Stationary Ti...] [2008-12-01 17:48:47] [b943bd7078334192ff8343563ee31113]
- RMP     [Spectral Analysis] [Non Stationary Ti...] [2008-12-01 19:56:04] [b943bd7078334192ff8343563ee31113]
- RMPD      [Cross Correlation Function] [Non Stationary Ti...] [2008-12-01 20:13:53] [b943bd7078334192ff8343563ee31113]
- RMPD        [(Partial) Autocorrelation Function] [Non Stationary Ti...] [2008-12-01 20:27:10] [b943bd7078334192ff8343563ee31113]
-   PD          [(Partial) Autocorrelation Function] [Non Stationary Ti...] [2008-12-01 20:29:06] [b943bd7078334192ff8343563ee31113]
-   P             [(Partial) Autocorrelation Function] [Non Stationary Ti...] [2008-12-01 20:31:48] [b943bd7078334192ff8343563ee31113]
- RMP               [Variance Reduction Matrix] [Non Stationary Ti...] [2008-12-01 20:34:07] [b943bd7078334192ff8343563ee31113]
- RMP                 [Spectral Analysis] [Non Stationary Ti...] [2008-12-01 20:37:58] [b943bd7078334192ff8343563ee31113]
- RMP                     [Standard Deviation-Mean Plot] [Non Stationary Ti...] [2008-12-01 20:41:45] [620b6ad5c4696049e39cb73ce029682c] [Current]
- RMPD                      [(Partial) Autocorrelation Function] [Non Stationary Ti...] [2008-12-02 07:15:23] [b943bd7078334192ff8343563ee31113]
-   PD                        [(Partial) Autocorrelation Function] [Non Stationary Ti...] [2008-12-02 07:17:05] [b943bd7078334192ff8343563ee31113]
-   P                           [(Partial) Autocorrelation Function] [Non Stationary Ti...] [2008-12-02 07:19:06] [b943bd7078334192ff8343563ee31113]
- RMP                             [Variance Reduction Matrix] [Non Stationary Ti...] [2008-12-02 07:22:03] [b943bd7078334192ff8343563ee31113]
- RMP                               [Spectral Analysis] [Non Stationary Ti...] [2008-12-02 07:25:43] [b943bd7078334192ff8343563ee31113]
- RMP                                 [Standard Deviation-Mean Plot] [Non Stationary Ti...] [2008-12-02 07:32:20] [b943bd7078334192ff8343563ee31113]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1593
1477.9
1733.7
1569.7
1843.7
1950.3
1657.5
1772.1
1568.3
1809.8
1646.7
1808.5
1763.9
1625.5
1538.8
1342.4
1645.1
1619.9
1338.1
1505.5
1529.1
1511.9
1656.7
1694.4
1662.3
1588.7
1483.3
1585.6
1658.9
1584.4
1470.6
1618.7
1407.6
1473.9
1515.3
1485.4
1496.1
1493.5
1298.4
1375.3
1507.9
1455.3
1363.3
1392.8
1348.8
1880.3
1669.2
1543.6
1701.2
1516.5
1466.8
1484.1
1577.2
1684.5
1414.7
1674.5
1598.7
1739.1
1674.6
1671.8
1802
1526.8
1580.9
1634.8
1610.3
1712
1678.8
1708.1
1680.6
2056
1624
2021.4
1861.1
1750.8
1767.5
1710.3
2151.5
2047.9
1915.4
1984.7
1896.5
2170.8
2139.9
2330.5
2121.8
2226.8
1857.9
2155.9
2341.7
2290.2
2006.5
2111.9
1731.3
1762.2
1863.2
1943.5
1975.2

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=27371&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=27371&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27371&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
11702.6139.195278133080472.4
21564.275130.442569215867425.8
31544.5583333333382.5627745562384254.7
41485.375160.663901727120581.9
51600.30833333333107.028003628051324.4
61719.64166666667164.896664381795529.2
71977.24166666667194.114762018074620.2
82034.40833333333204.663206527359610.4

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 1702.6 & 139.195278133080 & 472.4 \tabularnewline
2 & 1564.275 & 130.442569215867 & 425.8 \tabularnewline
3 & 1544.55833333333 & 82.5627745562384 & 254.7 \tabularnewline
4 & 1485.375 & 160.663901727120 & 581.9 \tabularnewline
5 & 1600.30833333333 & 107.028003628051 & 324.4 \tabularnewline
6 & 1719.64166666667 & 164.896664381795 & 529.2 \tabularnewline
7 & 1977.24166666667 & 194.114762018074 & 620.2 \tabularnewline
8 & 2034.40833333333 & 204.663206527359 & 610.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27371&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]1702.6[/C][C]139.195278133080[/C][C]472.4[/C][/ROW]
[ROW][C]2[/C][C]1564.275[/C][C]130.442569215867[/C][C]425.8[/C][/ROW]
[ROW][C]3[/C][C]1544.55833333333[/C][C]82.5627745562384[/C][C]254.7[/C][/ROW]
[ROW][C]4[/C][C]1485.375[/C][C]160.663901727120[/C][C]581.9[/C][/ROW]
[ROW][C]5[/C][C]1600.30833333333[/C][C]107.028003628051[/C][C]324.4[/C][/ROW]
[ROW][C]6[/C][C]1719.64166666667[/C][C]164.896664381795[/C][C]529.2[/C][/ROW]
[ROW][C]7[/C][C]1977.24166666667[/C][C]194.114762018074[/C][C]620.2[/C][/ROW]
[ROW][C]8[/C][C]2034.40833333333[/C][C]204.663206527359[/C][C]610.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27371&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27371&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
11702.6139.195278133080472.4
21564.275130.442569215867425.8
31544.5583333333382.5627745562384254.7
41485.375160.663901727120581.9
51600.30833333333107.028003628051324.4
61719.64166666667164.896664381795529.2
71977.24166666667194.114762018074620.2
82034.40833333333204.663206527359610.4






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-124.351276939090
beta0.159840923636862
S.D.0.0526479499498993
T-STAT3.03603319386546
p-value0.0229194199366736

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -124.351276939090 \tabularnewline
beta & 0.159840923636862 \tabularnewline
S.D. & 0.0526479499498993 \tabularnewline
T-STAT & 3.03603319386546 \tabularnewline
p-value & 0.0229194199366736 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27371&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-124.351276939090[/C][/ROW]
[ROW][C]beta[/C][C]0.159840923636862[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0526479499498993[/C][/ROW]
[ROW][C]T-STAT[/C][C]3.03603319386546[/C][/ROW]
[ROW][C]p-value[/C][C]0.0229194199366736[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27371&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27371&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-124.351276939090
beta0.159840923636862
S.D.0.0526479499498993
T-STAT3.03603319386546
p-value0.0229194199366736






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-8.95038593265395
beta1.87084766059999
S.D.0.762570761027584
T-STAT2.45334302888689
p-value0.0495652726339761
Lambda-0.870847660599995

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -8.95038593265395 \tabularnewline
beta & 1.87084766059999 \tabularnewline
S.D. & 0.762570761027584 \tabularnewline
T-STAT & 2.45334302888689 \tabularnewline
p-value & 0.0495652726339761 \tabularnewline
Lambda & -0.870847660599995 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27371&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-8.95038593265395[/C][/ROW]
[ROW][C]beta[/C][C]1.87084766059999[/C][/ROW]
[ROW][C]S.D.[/C][C]0.762570761027584[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.45334302888689[/C][/ROW]
[ROW][C]p-value[/C][C]0.0495652726339761[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.870847660599995[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27371&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27371&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-8.95038593265395
beta1.87084766059999
S.D.0.762570761027584
T-STAT2.45334302888689
p-value0.0495652726339761
Lambda-0.870847660599995


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')