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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 computationThu, 20 Nov 2014 10:36:15 +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/2014/Nov/20/t1416479798o3366rc23e2fi1d.htm/, Retrieved Fri, 17 May 2024 04:10:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=256771, Retrieved Fri, 17 May 2024 04:10:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact108

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] [Werkloze beroepsb...] [2014-11-20 10:36:15] [30b408b6447afc100cbee3b5fe745b69] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
82
80
76
73
70
68
67
64
69
69
67
57
67
69
67
66
65
56
57
53
58
59
60
59
65
62
61
62
57
51
45
46
48
49
48
43
51
54
57
60
58
61
62
62
64
68
70
73
79
84
82
78
78
76
73
71
71
70
74
72
80
80
80
79
82
71
75
74
76
82
85
82
92
93
93
99
98
89
96
94
99
108
113
115
126
131
134
134
137
139
139
134
133
135
130
133

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' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=256771&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=256771&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=256771&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' @ jenkins.wessa.net






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
170.16666666666676.8600733276406125
261.33333333333335.2107115782314416
353.08333333333337.7981155454767422
461.66666666666676.4291005073286422
575.66666666666674.579268169663914
678.83333333333334.0414518843273814
799.08333333333338.4687482528232626
8133.753.6958207355188213

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 70.1666666666667 & 6.86007332764061 & 25 \tabularnewline
2 & 61.3333333333333 & 5.21071157823144 & 16 \tabularnewline
3 & 53.0833333333333 & 7.79811554547674 & 22 \tabularnewline
4 & 61.6666666666667 & 6.42910050732864 & 22 \tabularnewline
5 & 75.6666666666667 & 4.5792681696639 & 14 \tabularnewline
6 & 78.8333333333333 & 4.04145188432738 & 14 \tabularnewline
7 & 99.0833333333333 & 8.46874825282326 & 26 \tabularnewline
8 & 133.75 & 3.69582073551882 & 13 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=256771&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]70.1666666666667[/C][C]6.86007332764061[/C][C]25[/C][/ROW]
[ROW][C]2[/C][C]61.3333333333333[/C][C]5.21071157823144[/C][C]16[/C][/ROW]
[ROW][C]3[/C][C]53.0833333333333[/C][C]7.79811554547674[/C][C]22[/C][/ROW]
[ROW][C]4[/C][C]61.6666666666667[/C][C]6.42910050732864[/C][C]22[/C][/ROW]
[ROW][C]5[/C][C]75.6666666666667[/C][C]4.5792681696639[/C][C]14[/C][/ROW]
[ROW][C]6[/C][C]78.8333333333333[/C][C]4.04145188432738[/C][C]14[/C][/ROW]
[ROW][C]7[/C][C]99.0833333333333[/C][C]8.46874825282326[/C][C]26[/C][/ROW]
[ROW][C]8[/C][C]133.75[/C][C]3.69582073551882[/C][C]13[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=256771&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=256771&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
170.16666666666676.8600733276406125
261.33333333333335.2107115782314416
353.08333333333337.7981155454767422
461.66666666666676.4291005073286422
575.66666666666674.579268169663914
678.83333333333334.0414518843273814
799.08333333333338.4687482528232626
8133.753.6958207355188213






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha7.85991752358738
beta-0.0249312905763855
S.D.0.0257318858855571
T-STAT-0.968887033281109
p-value0.370019534115906

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 7.85991752358738 \tabularnewline
beta & -0.0249312905763855 \tabularnewline
S.D. & 0.0257318858855571 \tabularnewline
T-STAT & -0.968887033281109 \tabularnewline
p-value & 0.370019534115906 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=256771&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]7.85991752358738[/C][/ROW]
[ROW][C]beta[/C][C]-0.0249312905763855[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0257318858855571[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.968887033281109[/C][/ROW]
[ROW][C]p-value[/C][C]0.370019534115906[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=256771&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=256771&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)
alpha7.85991752358738
beta-0.0249312905763855
S.D.0.0257318858855571
T-STAT-0.968887033281109
p-value0.370019534115906






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha3.73309030096512
beta-0.462150111300932
S.D.0.379934836972326
T-STAT-1.21639309252022
p-value0.269511356604808
Lambda1.46215011130093

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 3.73309030096512 \tabularnewline
beta & -0.462150111300932 \tabularnewline
S.D. & 0.379934836972326 \tabularnewline
T-STAT & -1.21639309252022 \tabularnewline
p-value & 0.269511356604808 \tabularnewline
Lambda & 1.46215011130093 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=256771&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]3.73309030096512[/C][/ROW]
[ROW][C]beta[/C][C]-0.462150111300932[/C][/ROW]
[ROW][C]S.D.[/C][C]0.379934836972326[/C][/ROW]
[ROW][C]T-STAT[/C][C]-1.21639309252022[/C][/ROW]
[ROW][C]p-value[/C][C]0.269511356604808[/C][/ROW]
[ROW][C]Lambda[/C][C]1.46215011130093[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=256771&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=256771&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)
alpha3.73309030096512
beta-0.462150111300932
S.D.0.379934836972326
T-STAT-1.21639309252022
p-value0.269511356604808
Lambda1.46215011130093


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')