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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, 12 Dec 2008 02:46:43 -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/2008/Dec/12/t12290752823pgyzjasd7aw4vt.htm/, Retrieved Tue, 21 May 2024 03:47:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=32506, Retrieved Tue, 21 May 2024 03:47:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact263

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] [Run sequence plot...] [2008-12-02 21:55:47] [ed2ba3b6182103c15c0ab511ae4e6284]
- RMP   [Variance Reduction Matrix] [Variance reductio...] [2008-12-12 09:38:10] [ed2ba3b6182103c15c0ab511ae4e6284]
- RM        [Standard Deviation-Mean Plot] [Standard deviatio...] [2008-12-12 09:46:43] [a8228479d4547a92e2d3f176a5299609] [Current]
- RMP         [(Partial) Autocorrelation Function] [(P)ACF tabaksprod...] [2008-12-12 10:09:30] [ed2ba3b6182103c15c0ab511ae4e6284]
-   P           [(Partial) Autocorrelation Function] [] [2008-12-12 10:26:36] [ed2ba3b6182103c15c0ab511ae4e6284]
-                 [(Partial) Autocorrelation Function] [(P)ACF tabakspodu...] [2008-12-12 11:01:48] [ed2ba3b6182103c15c0ab511ae4e6284]
-   P               [(Partial) Autocorrelation Function] [(P)ACF tabaksprod...] [2008-12-13 12:52:28] [ed2ba3b6182103c15c0ab511ae4e6284]
- RMP           [Spectral Analysis] [Spectrale analyse...] [2008-12-12 10:30:46] [ed2ba3b6182103c15c0ab511ae4e6284]
-                 [Spectral Analysis] [Spectrale analyse...] [2008-12-12 11:05:23] [ed2ba3b6182103c15c0ab511ae4e6284]
- RMP               [ARIMA Backward Selection] [ARIMA backward se...] [2008-12-12 12:52:16] [ed2ba3b6182103c15c0ab511ae4e6284]
- RMPD                [ARIMA Forecasting] [] [2008-12-12 14:09:09] [a4ee3bef49b119f4bd2e925060c84f5e]
-   PD                [ARIMA Backward Selection] [] [2008-12-12 14:08:29] [a4ee3bef49b119f4bd2e925060c84f5e]
- RMP                 [(Partial) Autocorrelation Function] [] [2008-12-12 14:06:36] [a4ee3bef49b119f4bd2e925060c84f5e]
-   PD                [ARIMA Backward Selection] [] [2008-12-12 17:29:27] [a4ee3bef49b119f4bd2e925060c84f5e]
- RMPD                [ARIMA Forecasting] [] [2008-12-12 17:26:53] [a4ee3bef49b119f4bd2e925060c84f5e]
- RMP               [ARIMA Backward Selection] [ARIMA blog] [2008-12-12 13:09:51] [ed2ba3b6182103c15c0ab511ae4e6284]
-   P               [Spectral Analysis] [Spectrale analyse...] [2008-12-13 13:07:40] [ed2ba3b6182103c15c0ab511ae4e6284]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
44.9
48.1
52.3
48.9
52.6
60.3
50.5
41.6
56
51.4
52.9
54.9
43.9
51
51.9
54.3
50.3
57.2
48.8
41.1
58
63
53.8
54.7
55.5
56.1
69.6
69.4
57.2
68
53.3
47.9
60.8
61.7
57.8
51.4
50.5
48.1
58.7
54
56.1
60.4
51.2
50.7
56.4
53.3
52.6
47.7
49.5
48.5
55.3
49.8
57.4
64.6
53
41.5
55.9
58.4
53.5
50.6
58.5

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=32506&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=32506&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32506&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
151.24.9806899850595718.7
252.33333333333336.0082266833723821.9
359.05833333333337.0762674204739221.7
453.30833333333333.9932803406207112.7
553.16666666666675.8577583880980723.1

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 51.2 & 4.98068998505957 & 18.7 \tabularnewline
2 & 52.3333333333333 & 6.00822668337238 & 21.9 \tabularnewline
3 & 59.0583333333333 & 7.07626742047392 & 21.7 \tabularnewline
4 & 53.3083333333333 & 3.99328034062071 & 12.7 \tabularnewline
5 & 53.1666666666667 & 5.85775838809807 & 23.1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32506&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]51.2[/C][C]4.98068998505957[/C][C]18.7[/C][/ROW]
[ROW][C]2[/C][C]52.3333333333333[/C][C]6.00822668337238[/C][C]21.9[/C][/ROW]
[ROW][C]3[/C][C]59.0583333333333[/C][C]7.07626742047392[/C][C]21.7[/C][/ROW]
[ROW][C]4[/C][C]53.3083333333333[/C][C]3.99328034062071[/C][C]12.7[/C][/ROW]
[ROW][C]5[/C][C]53.1666666666667[/C][C]5.85775838809807[/C][C]23.1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32506&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32506&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
151.24.9806899850595718.7
252.33333333333336.0082266833723821.9
359.05833333333337.0762674204739221.7
453.30833333333333.9932803406207112.7
553.16666666666675.8577583880980723.1






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-8.01652329998243
beta0.252721157027515
S.D.0.163951009334190
T-STAT1.54144313019983
p-value0.220868279296568

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -8.01652329998243 \tabularnewline
beta & 0.252721157027515 \tabularnewline
S.D. & 0.163951009334190 \tabularnewline
T-STAT & 1.54144313019983 \tabularnewline
p-value & 0.220868279296568 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32506&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-8.01652329998243[/C][/ROW]
[ROW][C]beta[/C][C]0.252721157027515[/C][/ROW]
[ROW][C]S.D.[/C][C]0.163951009334190[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.54144313019983[/C][/ROW]
[ROW][C]p-value[/C][C]0.220868279296568[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32506&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32506&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-8.01652329998243
beta0.252721157027515
S.D.0.163951009334190
T-STAT1.54144313019983
p-value0.220868279296568






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-7.54113412503918
beta2.31978886723654
S.D.1.83274691457275
T-STAT1.26574424913291
p-value0.294968549889797
Lambda-1.31978886723654

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -7.54113412503918 \tabularnewline
beta & 2.31978886723654 \tabularnewline
S.D. & 1.83274691457275 \tabularnewline
T-STAT & 1.26574424913291 \tabularnewline
p-value & 0.294968549889797 \tabularnewline
Lambda & -1.31978886723654 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32506&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-7.54113412503918[/C][/ROW]
[ROW][C]beta[/C][C]2.31978886723654[/C][/ROW]
[ROW][C]S.D.[/C][C]1.83274691457275[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.26574424913291[/C][/ROW]
[ROW][C]p-value[/C][C]0.294968549889797[/C][/ROW]
[ROW][C]Lambda[/C][C]-1.31978886723654[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32506&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32506&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-7.54113412503918
beta2.31978886723654
S.D.1.83274691457275
T-STAT1.26574424913291
p-value0.294968549889797
Lambda-1.31978886723654


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')