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Description of Statistical Computation

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, 04 Dec 2009 11:48:55 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/04/t12599526317ljvxgz161opuuw.htm/, Retrieved Sat, 27 Apr 2024 13:17:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64028, Retrieved Sat, 27 Apr 2024 13:17:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130

Tree of Dependent Computations

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] [] [2009-11-27 14:40:44] [b98453cac15ba1066b407e146608df68]
-    D    [Standard Deviation-Mean Plot] [BBWS9-Regressieta...] [2009-12-01 20:06:37] [408e92805dcb18620260f240a7fb9d53]
-    D      [Standard Deviation-Mean Plot] [shw-ws9] [2009-12-04 12:49:51] [2663058f2a5dda519058ac6b2228468f]
-    D          [Standard Deviation-Mean Plot] [ws 9 regressie model] [2009-12-04 18:48:55] [4f297b039e1043ebee7ff7a83b1eaaaa] [Current]
- R PD            [Standard Deviation-Mean Plot] [ws9 lambda] [2009-12-04 20:23:06] [95cead3ebb75668735f848316249436a]
-   PD              [Standard Deviation-Mean Plot] [paper st dev-mean...] [2009-12-13 13:39:01] [95cead3ebb75668735f848316249436a]
-   PD                [Standard Deviation-Mean Plot] [st dev mean plot 2] [2009-12-13 17:54:01] [95cead3ebb75668735f848316249436a]
-    D            [Standard Deviation-Mean Plot] [sdmp icp] [2009-12-10 18:34:21] [134dc66689e3d457a82860db6471d419]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100.01
103.84
104.48
95.43
104.80
108.64
105.65
108.42
115.35
113.64
115.24
100.33
101.29
104.48
99.26
100.11
103.52
101.18
96.39
97.56
96.39
85.10
79.77
79.13
80.84
82.75
92.55
96.60
96.92
95.32
98.52
100.22
104.91
103.10
97.13
103.42
111.72
118.11
111.62
100.22
102.03
105.76
107.68
110.77
105.44
112.26
114.07
117.90
124.72
126.42
134.73
135.79
143.36
140.37
144.74
151.98
150.92
163.38
154.43
146.66
157.95
162.10
180.42
179.57
171.58
185.43
190.64
203.00
202.36
193.41
186.17
192.24
209.60
206.41
209.82
230.37
235.80
232.07
244.64
242.19
217.48
209.39
211.73
221.00
203.11
214.71
224.19
238.04
238.36
246.24
259.87
249.97
266.48
282.98
306.31
301.73
314.62
332.62
355.51
370.32
408.13
433.58
440.51
386.29
342.84
254.97
203.42
170.09
174.03
167.85
177.01
188.19
211.20
240.91
230.26
251.25
241.66

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' @ 72.249.127.135

\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' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64028&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' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64028&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64028&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' @ 72.249.127.135






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
1106.3191666666676.2600151878022719.92
295.34833333333338.9201435305750925.35
396.02333333333337.5633269895936124.07
4109.7983333333335.7117515755332117.89
5143.12511.449772129530938.66
6183.73916666666714.300262208869645.05
7222.54166666666713.848736623266638.23
8252.66583333333332.5032847174596103.2
9334.40833333333386.281188809753270.42

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 106.319166666667 & 6.26001518780227 & 19.92 \tabularnewline
2 & 95.3483333333333 & 8.92014353057509 & 25.35 \tabularnewline
3 & 96.0233333333333 & 7.56332698959361 & 24.07 \tabularnewline
4 & 109.798333333333 & 5.71175157553321 & 17.89 \tabularnewline
5 & 143.125 & 11.4497721295309 & 38.66 \tabularnewline
6 & 183.739166666667 & 14.3002622088696 & 45.05 \tabularnewline
7 & 222.541666666667 & 13.8487366232666 & 38.23 \tabularnewline
8 & 252.665833333333 & 32.5032847174596 & 103.2 \tabularnewline
9 & 334.408333333333 & 86.281188809753 & 270.42 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64028&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]106.319166666667[/C][C]6.26001518780227[/C][C]19.92[/C][/ROW]
[ROW][C]2[/C][C]95.3483333333333[/C][C]8.92014353057509[/C][C]25.35[/C][/ROW]
[ROW][C]3[/C][C]96.0233333333333[/C][C]7.56332698959361[/C][C]24.07[/C][/ROW]
[ROW][C]4[/C][C]109.798333333333[/C][C]5.71175157553321[/C][C]17.89[/C][/ROW]
[ROW][C]5[/C][C]143.125[/C][C]11.4497721295309[/C][C]38.66[/C][/ROW]
[ROW][C]6[/C][C]183.739166666667[/C][C]14.3002622088696[/C][C]45.05[/C][/ROW]
[ROW][C]7[/C][C]222.541666666667[/C][C]13.8487366232666[/C][C]38.23[/C][/ROW]
[ROW][C]8[/C][C]252.665833333333[/C][C]32.5032847174596[/C][C]103.2[/C][/ROW]
[ROW][C]9[/C][C]334.408333333333[/C][C]86.281188809753[/C][C]270.42[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64028&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64028&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
1106.3191666666676.2600151878022719.92
295.34833333333338.9201435305750925.35
396.02333333333337.5633269895936124.07
4109.7983333333335.7117515755332117.89
5143.12511.449772129530938.66
6183.73916666666714.300262208869645.05
7222.54166666666713.848736623266638.23
8252.66583333333332.5032847174596103.2
9334.40833333333386.281188809753270.42






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-25.7005255404872
beta0.270823550537291
S.D.0.0559715167650007
T-STAT4.83859588215838
p-value0.00188078879011415

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -25.7005255404872 \tabularnewline
beta & 0.270823550537291 \tabularnewline
S.D. & 0.0559715167650007 \tabularnewline
T-STAT & 4.83859588215838 \tabularnewline
p-value & 0.00188078879011415 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64028&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-25.7005255404872[/C][/ROW]
[ROW][C]beta[/C][C]0.270823550537291[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0559715167650007[/C][/ROW]
[ROW][C]T-STAT[/C][C]4.83859588215838[/C][/ROW]
[ROW][C]p-value[/C][C]0.00188078879011415[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64028&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64028&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-25.7005255404872
beta0.270823550537291
S.D.0.0559715167650007
T-STAT4.83859588215838
p-value0.00188078879011415






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-6.04442273671118
beta1.71430685480952
S.D.0.290808482639666
T-STAT5.8949685347854
p-value0.000602532396120366
Lambda-0.71430685480952

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -6.04442273671118 \tabularnewline
beta & 1.71430685480952 \tabularnewline
S.D. & 0.290808482639666 \tabularnewline
T-STAT & 5.8949685347854 \tabularnewline
p-value & 0.000602532396120366 \tabularnewline
Lambda & -0.71430685480952 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64028&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-6.04442273671118[/C][/ROW]
[ROW][C]beta[/C][C]1.71430685480952[/C][/ROW]
[ROW][C]S.D.[/C][C]0.290808482639666[/C][/ROW]
[ROW][C]T-STAT[/C][C]5.8949685347854[/C][/ROW]
[ROW][C]p-value[/C][C]0.000602532396120366[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.71430685480952[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64028&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64028&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-6.04442273671118
beta1.71430685480952
S.D.0.290808482639666
T-STAT5.8949685347854
p-value0.000602532396120366
Lambda-0.71430685480952


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')