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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 computationFri, 17 Dec 2010 20:15:09 +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/2010/Dec/17/t129261682305z9xxuk8ldmmhw.htm/, Retrieved Fri, 03 May 2024 23:10:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=111705, Retrieved Fri, 03 May 2024 23:10:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact166
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [Bouwvergunningen] [2009-11-02 16:57:06] [11ac052cc87d77b9933b02bea117068e]
-   P   [Univariate Data Series] [Bouwvergunningen ...] [2009-11-11 14:29:30] [11ac052cc87d77b9933b02bea117068e]
- RMPD    [Variance Reduction Matrix] [Workshop 6] [2010-12-16 20:00:53] [29e492448d11757ae0fad5ef6e7f8e86]
- RMPD        [Standard Deviation-Mean Plot] [] [2010-12-17 20:15:09] [0956ee981dded61b2e7128dae94e5715] [Current]
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Dataseries X:
2617.2
2506.13
2679.07
2589.73
2457.46
2517.3
2386.53
2453.37
2529.66
2475.14
2525.93
2480.93
2229.85
2169.14
2030.98
2071.37
1953.35
1748.74
1696.58
1900.09
1908.64
1881.46
2100.18
2672.2
3136
2994.38
3168.22
3751.41
3925.43
3719.52
3757.12
3722.23
4127.47
4162.5
4441.82
4325.29
4350.83
4384.47
4639.4
4697.86
4614.76
4471.65
4305.23
4433.57
4388.53
4140.3
4144.38
4070.78
3906.01
3795.91
3703.32
3675.8
3911.06
3912.28
3839.25
3744.63
3549.25
3394.14
3264.26
3328.8
3223.98
3228.01
3112.83
3051.67
3039.71
3125.67
3106.54




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111705&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111705&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111705&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 time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
12518.2041666666779.5480180266592292.54
22030.215257.289757577999975.62
33769.2825470.6577306112061447.44
44386.81333333333202.09630307146627.08
53668.72583333333232.657539918784648.02

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 2518.20416666667 & 79.5480180266592 & 292.54 \tabularnewline
2 & 2030.215 & 257.289757577999 & 975.62 \tabularnewline
3 & 3769.2825 & 470.657730611206 & 1447.44 \tabularnewline
4 & 4386.81333333333 & 202.09630307146 & 627.08 \tabularnewline
5 & 3668.72583333333 & 232.657539918784 & 648.02 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111705&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]2518.20416666667[/C][C]79.5480180266592[/C][C]292.54[/C][/ROW]
[ROW][C]2[/C][C]2030.215[/C][C]257.289757577999[/C][C]975.62[/C][/ROW]
[ROW][C]3[/C][C]3769.2825[/C][C]470.657730611206[/C][C]1447.44[/C][/ROW]
[ROW][C]4[/C][C]4386.81333333333[/C][C]202.09630307146[/C][C]627.08[/C][/ROW]
[ROW][C]5[/C][C]3668.72583333333[/C][C]232.657539918784[/C][C]648.02[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111705&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111705&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
12518.2041666666779.5480180266592292.54
22030.215257.289757577999975.62
33769.2825470.6577306112061447.44
44386.81333333333202.09630307146627.08
53668.72583333333232.657539918784648.02







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha101.262062240348
beta0.0449476707449458
S.D.0.0803546038909565
T-STAT0.559366465248725
p-value0.61495591900432

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 101.262062240348 \tabularnewline
beta & 0.0449476707449458 \tabularnewline
S.D. & 0.0803546038909565 \tabularnewline
T-STAT & 0.559366465248725 \tabularnewline
p-value & 0.61495591900432 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111705&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]101.262062240348[/C][/ROW]
[ROW][C]beta[/C][C]0.0449476707449458[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0803546038909565[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.559366465248725[/C][/ROW]
[ROW][C]p-value[/C][C]0.61495591900432[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111705&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111705&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)
alpha101.262062240348
beta0.0449476707449458
S.D.0.0803546038909565
T-STAT0.559366465248725
p-value0.61495591900432







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-0.0756509981875061
beta0.675772159185053
S.D.1.09124339949797
T-STAT0.619268038180979
p-value0.579587715687442
Lambda0.324227840814947

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -0.0756509981875061 \tabularnewline
beta & 0.675772159185053 \tabularnewline
S.D. & 1.09124339949797 \tabularnewline
T-STAT & 0.619268038180979 \tabularnewline
p-value & 0.579587715687442 \tabularnewline
Lambda & 0.324227840814947 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111705&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.0756509981875061[/C][/ROW]
[ROW][C]beta[/C][C]0.675772159185053[/C][/ROW]
[ROW][C]S.D.[/C][C]1.09124339949797[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.619268038180979[/C][/ROW]
[ROW][C]p-value[/C][C]0.579587715687442[/C][/ROW]
[ROW][C]Lambda[/C][C]0.324227840814947[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111705&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111705&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-0.0756509981875061
beta0.675772159185053
S.D.1.09124339949797
T-STAT0.619268038180979
p-value0.579587715687442
Lambda0.324227840814947



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')