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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, 10 Dec 2016 18:40:30 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/10/t1481391654w6wlx591glqyuda.htm/, Retrieved Mon, 06 May 2024 02:23:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298739, Retrieved Mon, 06 May 2024 02:23:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact62
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Standard Deviation-Mean Plot] [""N2582"" deel 2] [2016-12-10 17:40:30] [afe7f6443461a2cd6ee0b843643e84a9] [Current]
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Dataseries X:
4028.8
4076.6
4125.8
4177.2
4183
4222.6
4255.8
4260.8
4279.2
4328.8
4356.6
4393
4419.4
4426.2
4467.2
4517.4
4517
4560.4
4589
4596
4621.2
4654.6
4708.6
4774.4
4824.8
4839
4869.8
4895.8
4895.8
4968.8
5010
5032.4
5054
5083.8
5117.4
5170.8
5182.2
5163.6
5212.6
5288
5303.4
5367.6
5433.8
5465.8
5493.8
5549.4
5590.2
5661.2
5699
5654.2
5671.8
5730.8
5693
5720.4
5747.8
5764.2
5783
5822.4
5836.2
5864.6
5913.4
5906.8
5954
6031.2
6011.2
6059.8
6091.6
6088
6082.2
6108
6151.4
6187
6190
6152.2
6183.8
6222.8
6165.8
6223.4
6292.8
6320.6
6344
6391.2
6443.4
6504
6520.2
6518.8
6563.8
6614
6555.6
6601.8
6632.4
6657.8
6674.4
6687
6697.6
6732
6736.4
6745.8
6805.2
6850.4
6807.2
6844.6
6850.8
6848.2
6837.8
6857.6
6900.8
6940.8
6937.4
6950.4
6978.8
6997.8
6934.8
6946.8
6956.2
6968.2
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298739&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298739&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298739&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
14224.01666666667111.145096267188364.2
24570.95109.290542716526355
34980.2115.047784538742346
45392.63333333333165.613741927267497.599999999999
55748.9567.0652938289518210.400000000001
66048.7166666666788.9040937122014280.2
76286.16666666667115.953433704564351.8
86621.2833333333371.0003051636634213.2
96835.4666666666757.3124979759427204.400000000001
106958.821.595766780949163
11NaNNA-Inf
12NaNNA-Inf

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 4224.01666666667 & 111.145096267188 & 364.2 \tabularnewline
2 & 4570.95 & 109.290542716526 & 355 \tabularnewline
3 & 4980.2 & 115.047784538742 & 346 \tabularnewline
4 & 5392.63333333333 & 165.613741927267 & 497.599999999999 \tabularnewline
5 & 5748.95 & 67.0652938289518 & 210.400000000001 \tabularnewline
6 & 6048.71666666667 & 88.9040937122014 & 280.2 \tabularnewline
7 & 6286.16666666667 & 115.953433704564 & 351.8 \tabularnewline
8 & 6621.28333333333 & 71.0003051636634 & 213.2 \tabularnewline
9 & 6835.46666666667 & 57.3124979759427 & 204.400000000001 \tabularnewline
10 & 6958.8 & 21.5957667809491 & 63 \tabularnewline
11 & NaN & NA & -Inf \tabularnewline
12 & NaN & NA & -Inf \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298739&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]4224.01666666667[/C][C]111.145096267188[/C][C]364.2[/C][/ROW]
[ROW][C]2[/C][C]4570.95[/C][C]109.290542716526[/C][C]355[/C][/ROW]
[ROW][C]3[/C][C]4980.2[/C][C]115.047784538742[/C][C]346[/C][/ROW]
[ROW][C]4[/C][C]5392.63333333333[/C][C]165.613741927267[/C][C]497.599999999999[/C][/ROW]
[ROW][C]5[/C][C]5748.95[/C][C]67.0652938289518[/C][C]210.400000000001[/C][/ROW]
[ROW][C]6[/C][C]6048.71666666667[/C][C]88.9040937122014[/C][C]280.2[/C][/ROW]
[ROW][C]7[/C][C]6286.16666666667[/C][C]115.953433704564[/C][C]351.8[/C][/ROW]
[ROW][C]8[/C][C]6621.28333333333[/C][C]71.0003051636634[/C][C]213.2[/C][/ROW]
[ROW][C]9[/C][C]6835.46666666667[/C][C]57.3124979759427[/C][C]204.400000000001[/C][/ROW]
[ROW][C]10[/C][C]6958.8[/C][C]21.5957667809491[/C][C]63[/C][/ROW]
[ROW][C]11[/C][C]NaN[/C][C]NA[/C][C]-Inf[/C][/ROW]
[ROW][C]12[/C][C]NaN[/C][C]NA[/C][C]-Inf[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298739&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298739&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
14224.01666666667111.145096267188364.2
24570.95109.290542716526355
34980.2115.047784538742346
45392.63333333333165.613741927267497.599999999999
55748.9567.0652938289518210.400000000001
66048.7166666666788.9040937122014280.2
76286.16666666667115.953433704564351.8
86621.2833333333371.0003051636634213.2
96835.4666666666757.3124979759427204.400000000001
106958.821.595766780949163
11NaNNA-Inf
12NaNNA-Inf







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha248.764226735964
beta-0.0271335206663231
S.D.0.0112912479660171
T-STAT-2.40305772647859
p-value0.0429711947399758

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 248.764226735964 \tabularnewline
beta & -0.0271335206663231 \tabularnewline
S.D. & 0.0112912479660171 \tabularnewline
T-STAT & -2.40305772647859 \tabularnewline
p-value & 0.0429711947399758 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298739&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]248.764226735964[/C][/ROW]
[ROW][C]beta[/C][C]-0.0271335206663231[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0112912479660171[/C][/ROW]
[ROW][C]T-STAT[/C][C]-2.40305772647859[/C][/ROW]
[ROW][C]p-value[/C][C]0.0429711947399758[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298739&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298739&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)
alpha248.764226735964
beta-0.0271335206663231
S.D.0.0112912479660171
T-STAT-2.40305772647859
p-value0.0429711947399758







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha22.3013624784228
beta-2.06924232045858
S.D.0.898196855884644
T-STAT-2.3037737294466
p-value0.0501743901265797
Lambda3.06924232045858

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 22.3013624784228 \tabularnewline
beta & -2.06924232045858 \tabularnewline
S.D. & 0.898196855884644 \tabularnewline
T-STAT & -2.3037737294466 \tabularnewline
p-value & 0.0501743901265797 \tabularnewline
Lambda & 3.06924232045858 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298739&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]22.3013624784228[/C][/ROW]
[ROW][C]beta[/C][C]-2.06924232045858[/C][/ROW]
[ROW][C]S.D.[/C][C]0.898196855884644[/C][/ROW]
[ROW][C]T-STAT[/C][C]-2.3037737294466[/C][/ROW]
[ROW][C]p-value[/C][C]0.0501743901265797[/C][/ROW]
[ROW][C]Lambda[/C][C]3.06924232045858[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298739&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298739&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)
alpha22.3013624784228
beta-2.06924232045858
S.D.0.898196855884644
T-STAT-2.3037737294466
p-value0.0501743901265797
Lambda3.06924232045858



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')