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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationTue, 06 Dec 2011 13:11:54 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/06/t1323195183z3a03hafwvu2gkq.htm/, Retrieved Mon, 29 Apr 2024 00:46:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=151765, Retrieved Mon, 29 Apr 2024 00:46:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W83
Estimated Impact69

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] [gemiddelde consum...] [2011-12-06 18:11:54] [bd8cebb9d7961275d2f6ed94788b7e5f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
20.98
20.1
20.61
20.27
20.08
23.58
22.31
22.89
21.78
22.19
22.58
22.78
25.06
25.16
25.47
25.34
24.2
25.32
25.57
25.76
24.79
23.14
22.66
22.06
24.26
23.15
22.92
21.43
21.56
23.48
24.35
24.83
24.19
23.58
23.58
24.35
27.18
25.69
24.81
23.26
23.49
26.86
27.12
27.66
26.26
25.51
24.63
23.57
27.63
25.85
26.09
24.47
24.19
25.09
25.26
25.58
24.76
25.02
24.24
24.14
28.69
26.74
26.48
24.45
23.88
26.58
26.23
28.63
26.81
26.56
26.64
26.8
28.37
27.13
28.44
28.62
27.28
31.32
31.26
31.41
31.76
32.72
32.15
33.62
35.97
33.78
33.77
32.75
32.55
33.22
32.88
31.56
30.27
28.65
27.89
27.07
30.8
28.38
27.5
28
28.02
29.2
27.59
27.22
27.16
26.31
25.67
26.41
28.34
25.43
23.72
23.33
23.8
27.7
26.28
27.51
27.93
28.76
28.65
29.52
31.23
27.9
27.87
27.52
27.59
31.2
30.22
30.62
31.52
30.59
31.42
31.95

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'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=151765&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=151765&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151765&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'Gertrude Mary Cox' @ cox.wessa.net






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
121.67916666666671.222534315066513.5
224.54416666666671.24827487017693.7
323.47333333333331.077862643748213.4
425.50333333333331.554795124377454.4
525.19333333333331.004682974269933.49
626.54083333333331.372671306304434.81
730.342.231672833630336.49
831.69666666666672.691600852934028.9
927.68833333333331.37524763610275.13
1026.74752.179796174791666.19
1129.96916666666671.725664342866954.43

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 21.6791666666667 & 1.22253431506651 & 3.5 \tabularnewline
2 & 24.5441666666667 & 1.2482748701769 & 3.7 \tabularnewline
3 & 23.4733333333333 & 1.07786264374821 & 3.4 \tabularnewline
4 & 25.5033333333333 & 1.55479512437745 & 4.4 \tabularnewline
5 & 25.1933333333333 & 1.00468297426993 & 3.49 \tabularnewline
6 & 26.5408333333333 & 1.37267130630443 & 4.81 \tabularnewline
7 & 30.34 & 2.23167283363033 & 6.49 \tabularnewline
8 & 31.6966666666667 & 2.69160085293402 & 8.9 \tabularnewline
9 & 27.6883333333333 & 1.3752476361027 & 5.13 \tabularnewline
10 & 26.7475 & 2.17979617479166 & 6.19 \tabularnewline
11 & 29.9691666666667 & 1.72566434286695 & 4.43 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151765&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]21.6791666666667[/C][C]1.22253431506651[/C][C]3.5[/C][/ROW]
[ROW][C]2[/C][C]24.5441666666667[/C][C]1.2482748701769[/C][C]3.7[/C][/ROW]
[ROW][C]3[/C][C]23.4733333333333[/C][C]1.07786264374821[/C][C]3.4[/C][/ROW]
[ROW][C]4[/C][C]25.5033333333333[/C][C]1.55479512437745[/C][C]4.4[/C][/ROW]
[ROW][C]5[/C][C]25.1933333333333[/C][C]1.00468297426993[/C][C]3.49[/C][/ROW]
[ROW][C]6[/C][C]26.5408333333333[/C][C]1.37267130630443[/C][C]4.81[/C][/ROW]
[ROW][C]7[/C][C]30.34[/C][C]2.23167283363033[/C][C]6.49[/C][/ROW]
[ROW][C]8[/C][C]31.6966666666667[/C][C]2.69160085293402[/C][C]8.9[/C][/ROW]
[ROW][C]9[/C][C]27.6883333333333[/C][C]1.3752476361027[/C][C]5.13[/C][/ROW]
[ROW][C]10[/C][C]26.7475[/C][C]2.17979617479166[/C][C]6.19[/C][/ROW]
[ROW][C]11[/C][C]29.9691666666667[/C][C]1.72566434286695[/C][C]4.43[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151765&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151765&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
121.67916666666671.222534315066513.5
224.54416666666671.24827487017693.7
323.47333333333331.077862643748213.4
425.50333333333331.554795124377454.4
525.19333333333331.004682974269933.49
626.54083333333331.372671306304434.81
730.342.231672833630336.49
831.69666666666672.691600852934028.9
927.68833333333331.37524763610275.13
1026.74752.179796174791666.19
1129.96916666666671.725664342866954.43






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-2.17055866037496
beta0.141664525895602
S.D.0.0353471639285361
T-STAT4.00780459167855
p-value0.00307403218056104

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -2.17055866037496 \tabularnewline
beta & 0.141664525895602 \tabularnewline
S.D. & 0.0353471639285361 \tabularnewline
T-STAT & 4.00780459167855 \tabularnewline
p-value & 0.00307403218056104 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151765&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-2.17055866037496[/C][/ROW]
[ROW][C]beta[/C][C]0.141664525895602[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0353471639285361[/C][/ROW]
[ROW][C]T-STAT[/C][C]4.00780459167855[/C][/ROW]
[ROW][C]p-value[/C][C]0.00307403218056104[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151765&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151765&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-2.17055866037496
beta0.141664525895602
S.D.0.0353471639285361
T-STAT4.00780459167855
p-value0.00307403218056104






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-6.73061277934803
beta2.18382407575604
S.D.0.567833592627906
T-STAT3.84588742918398
p-value0.0039309604160759
Lambda-1.18382407575604

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -6.73061277934803 \tabularnewline
beta & 2.18382407575604 \tabularnewline
S.D. & 0.567833592627906 \tabularnewline
T-STAT & 3.84588742918398 \tabularnewline
p-value & 0.0039309604160759 \tabularnewline
Lambda & -1.18382407575604 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151765&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-6.73061277934803[/C][/ROW]
[ROW][C]beta[/C][C]2.18382407575604[/C][/ROW]
[ROW][C]S.D.[/C][C]0.567833592627906[/C][/ROW]
[ROW][C]T-STAT[/C][C]3.84588742918398[/C][/ROW]
[ROW][C]p-value[/C][C]0.0039309604160759[/C][/ROW]
[ROW][C]Lambda[/C][C]-1.18382407575604[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151765&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151765&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-6.73061277934803
beta2.18382407575604
S.D.0.567833592627906
T-STAT3.84588742918398
p-value0.0039309604160759
Lambda-1.18382407575604


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')