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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 computationSun, 02 Dec 2012 06:35:02 -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/2012/Dec/02/t1354448150t5ynqjcbqixbg93.htm/, Retrieved Fri, 29 Mar 2024 11:09:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=195439, Retrieved Fri, 29 Mar 2024 11:09:50 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact137
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
- R PD      [Standard Deviation-Mean Plot] [ws9] [2012-12-02 11:35:02] [2bcb0f1dab9cffb75c9fd882cacbd29a] [Current]
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Dataseries X:
88.1
101.7
114.8
103.4
96.4
110
71.1
79.4
119.2
99.1
113.2
103.6
97.5
102.4
120.8
89.5
101.7
112.5
72.4
84.7
117.2
112.8
111.3
102.3
95.2
103
116.4
95.1
100.7
112.4
75.3
93.3
118.6
118.7
110.7
113.3
89.5
106.3
115.1
105.7
95.8
114.7
79.6
80.6
125
127.5
99.5
104.3
90
96
108.9
95.8
87.2
108.4
74.9
80.8
119.1
107.9
106.9
96.8
93.7
95.2
112.7
98.5
91.5
112
76.7
84.7
114.9
108.4
104.6
111.3
90.8
109.1
121
95.2
110.5
102.4
86.7
99.1
126
110.3
104.6
103.1
102




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ yule.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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=195439&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=195439&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=195439&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 time2 seconds
R Server'George Udny Yule' @ yule.wessa.net







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
110014.473423921104548.1
2102.09166666666714.302095099754648.4
3104.39166666666713.107351048749443.4
4103.63333333333315.603806839319347.9
597.72513.03443864955844.2
6100.3512.282470879635438.2
7104.911.480972401008939.3

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 100 & 14.4734239211045 & 48.1 \tabularnewline
2 & 102.091666666667 & 14.3020950997546 & 48.4 \tabularnewline
3 & 104.391666666667 & 13.1073510487494 & 43.4 \tabularnewline
4 & 103.633333333333 & 15.6038068393193 & 47.9 \tabularnewline
5 & 97.725 & 13.034438649558 & 44.2 \tabularnewline
6 & 100.35 & 12.2824708796354 & 38.2 \tabularnewline
7 & 104.9 & 11.4809724010089 & 39.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=195439&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]100[/C][C]14.4734239211045[/C][C]48.1[/C][/ROW]
[ROW][C]2[/C][C]102.091666666667[/C][C]14.3020950997546[/C][C]48.4[/C][/ROW]
[ROW][C]3[/C][C]104.391666666667[/C][C]13.1073510487494[/C][C]43.4[/C][/ROW]
[ROW][C]4[/C][C]103.633333333333[/C][C]15.6038068393193[/C][C]47.9[/C][/ROW]
[ROW][C]5[/C][C]97.725[/C][C]13.034438649558[/C][C]44.2[/C][/ROW]
[ROW][C]6[/C][C]100.35[/C][C]12.2824708796354[/C][C]38.2[/C][/ROW]
[ROW][C]7[/C][C]104.9[/C][C]11.4809724010089[/C][C]39.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=195439&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=195439&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
110014.473423921104548.1
2102.09166666666714.302095099754648.4
3104.39166666666713.107351048749443.4
4103.63333333333315.603806839319347.9
597.72513.03443864955844.2
6100.3512.282470879635438.2
7104.911.480972401008939.3







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha16.5486259867624
beta-0.030228684579879
S.D.0.238960714237417
T-STAT-0.126500645415068
p-value0.904265218939706

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 16.5486259867624 \tabularnewline
beta & -0.030228684579879 \tabularnewline
S.D. & 0.238960714237417 \tabularnewline
T-STAT & -0.126500645415068 \tabularnewline
p-value & 0.904265218939706 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=195439&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]16.5486259867624[/C][/ROW]
[ROW][C]beta[/C][C]-0.030228684579879[/C][/ROW]
[ROW][C]S.D.[/C][C]0.238960714237417[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.126500645415068[/C][/ROW]
[ROW][C]p-value[/C][C]0.904265218939706[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=195439&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=195439&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)
alpha16.5486259867624
beta-0.030228684579879
S.D.0.238960714237417
T-STAT-0.126500645415068
p-value0.904265218939706







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha4.06161772455788
beta-0.317065099797673
S.D.1.80089134023955
T-STAT-0.176060094639301
p-value0.867155361537285
Lambda1.31706509979767

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 4.06161772455788 \tabularnewline
beta & -0.317065099797673 \tabularnewline
S.D. & 1.80089134023955 \tabularnewline
T-STAT & -0.176060094639301 \tabularnewline
p-value & 0.867155361537285 \tabularnewline
Lambda & 1.31706509979767 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=195439&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]4.06161772455788[/C][/ROW]
[ROW][C]beta[/C][C]-0.317065099797673[/C][/ROW]
[ROW][C]S.D.[/C][C]1.80089134023955[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.176060094639301[/C][/ROW]
[ROW][C]p-value[/C][C]0.867155361537285[/C][/ROW]
[ROW][C]Lambda[/C][C]1.31706509979767[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=195439&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=195439&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)
alpha4.06161772455788
beta-0.317065099797673
S.D.1.80089134023955
T-STAT-0.176060094639301
p-value0.867155361537285
Lambda1.31706509979767



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')