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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 computationThu, 27 Nov 2008 15:08:29 -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/Nov/27/t1227823771vx1agprqpqxculh.htm/, Retrieved Sun, 19 May 2024 11:49:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=25921, Retrieved Sun, 19 May 2024 11:49:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact227

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Law of Averages] [Random Walk Simul...] [2008-11-25 18:40:39] [b98453cac15ba1066b407e146608df68]
F       [Law of Averages] [Random Walk Simul...] [2008-11-27 19:45:04] [58bf45a666dc5198906262e8815a9722]
F RMPD      [Standard Deviation-Mean Plot] [Standard Deviatio...] [2008-11-27 22:08:29] [63db34dadd44fb018112addcdefe949f] [Current]
-    D        [Standard Deviation-Mean Plot] [Standard Deviatio...] [2008-11-27 22:18:06] [58bf45a666dc5198906262e8815a9722]
- RMP         [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-11-27 22:25:58] [58bf45a666dc5198906262e8815a9722]
F   P           [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-11-28 10:12:46] [58bf45a666dc5198906262e8815a9722]
F   P           [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-11-28 10:15:39] [58bf45a666dc5198906262e8815a9722]
F   P           [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-11-28 10:17:42] [58bf45a666dc5198906262e8815a9722]
F   P           [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-11-28 10:20:24] [58bf45a666dc5198906262e8815a9722]
-   PD          [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-11-28 10:23:41] [58bf45a666dc5198906262e8815a9722]
-   PD          [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-11-28 10:26:03] [58bf45a666dc5198906262e8815a9722]
-   PD          [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-11-28 10:26:03] [58bf45a666dc5198906262e8815a9722]
-   PD          [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-11-28 10:29:39] [58bf45a666dc5198906262e8815a9722]
-   PD          [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-11-28 10:31:19] [58bf45a666dc5198906262e8815a9722]
- RMP         [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-11-27 22:29:15] [58bf45a666dc5198906262e8815a9722]
- RMP         [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-11-27 22:31:47] [58bf45a666dc5198906262e8815a9722]
- RMP         [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-11-27 22:33:51] [58bf45a666dc5198906262e8815a9722]
- RMPD        [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-11-27 22:37:06] [58bf45a666dc5198906262e8815a9722]
- RMPD        [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-11-27 22:39:04] [58bf45a666dc5198906262e8815a9722]
- RMPD        [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-11-27 22:41:03] [58bf45a666dc5198906262e8815a9722]
- RMPD        [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2008-11-27 22:42:39] [58bf45a666dc5198906262e8815a9722]
- RM D        [Variance Reduction Matrix] [Variance Reductio...] [2008-11-27 22:45:11] [58bf45a666dc5198906262e8815a9722]
F RM          [Variance Reduction Matrix] [Variance Reductio...] [2008-11-27 22:46:51] [58bf45a666dc5198906262e8815a9722]
F RMP         [Spectral Analysis] [Spectral Analysis...] [2008-11-27 22:49:42] [58bf45a666dc5198906262e8815a9722]
- RMP         [Spectral Analysis] [Spectral Analysis...] [2008-11-27 22:53:13] [58bf45a666dc5198906262e8815a9722]
- RMP         [Spectral Analysis] [Spectral Analysis...] [2008-11-27 22:55:39] [58bf45a666dc5198906262e8815a9722]
- RMP         [Spectral Analysis] [Spectral Analysis...] [2008-11-27 22:58:38] [58bf45a666dc5198906262e8815a9722]
- RMPD        [Spectral Analysis] [Spectral Analysis...] [2008-11-27 23:01:45] [58bf45a666dc5198906262e8815a9722]
- RMPD        [Spectral Analysis] [Spectral Analysis...] [2008-11-27 23:04:22] [58bf45a666dc5198906262e8815a9722]
- R P           [Spectral Analysis] [Q4] [2008-12-01 15:46:23] [84dda5145c389bd11bcc662bd33fe4ba]
F R P           [Spectral Analysis] [Q4] [2008-12-01 15:46:23] [84dda5145c389bd11bcc662bd33fe4ba]
F   P           [Spectral Analysis] [Q4] [2008-12-01 15:52:17] [43d870b30ac8a7afeb5de9ee11dcfc1a]
Feedback Forum
2008-12-04 15:28:29 [Matthieu Blondeau] [reply] 
De studente antwoordt correct op deze vraag. De SMP verdeelt de data in sections (1 section = 1 jaar). De range is de grootste waarde - kleinste waarde. Indien de range groter wordt dan zal de spreiding groter worden.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
106
82
114
118
105
105
103
107
123
112
104
122
108
94
120
118
117
113
106
108
122
115
110
120
104
96
121
111
120
114
107
108
127
105
119
121
106
97
119
122
121
106
114
112
127
109
118
123
115
105
116
131
121
104
127
126
124
132
117
123

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25921&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25921&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25921&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'George Udny Yule' @ 72.249.76.132






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
1108.41666666666710.916695581733641
2112.5833333333337.9253142562680828
3112.759.0867035727034631
4114.58.7749643873921230
5120.0833333333339.0800313709243228

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 108.416666666667 & 10.9166955817336 & 41 \tabularnewline
2 & 112.583333333333 & 7.92531425626808 & 28 \tabularnewline
3 & 112.75 & 9.08670357270346 & 31 \tabularnewline
4 & 114.5 & 8.77496438739212 & 30 \tabularnewline
5 & 120.083333333333 & 9.08003137092432 & 28 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25921&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]108.416666666667[/C][C]10.9166955817336[/C][C]41[/C][/ROW]
[ROW][C]2[/C][C]112.583333333333[/C][C]7.92531425626808[/C][C]28[/C][/ROW]
[ROW][C]3[/C][C]112.75[/C][C]9.08670357270346[/C][C]31[/C][/ROW]
[ROW][C]4[/C][C]114.5[/C][C]8.77496438739212[/C][C]30[/C][/ROW]
[ROW][C]5[/C][C]120.083333333333[/C][C]9.08003137092432[/C][C]28[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25921&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25921&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
1108.41666666666710.916695581733641
2112.5833333333337.9253142562680828
3112.759.0867035727034631
4114.58.7749643873921230
5120.0833333333339.0800313709243228






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha22.9217115516362
beta-0.121099440332832
S.D.0.131789752830948
T-STAT-0.918883583370638
p-value0.425921163883119

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 22.9217115516362 \tabularnewline
beta & -0.121099440332832 \tabularnewline
S.D. & 0.131789752830948 \tabularnewline
T-STAT & -0.918883583370638 \tabularnewline
p-value & 0.425921163883119 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25921&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]22.9217115516362[/C][/ROW]
[ROW][C]beta[/C][C]-0.121099440332832[/C][/ROW]
[ROW][C]S.D.[/C][C]0.131789752830948[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.918883583370638[/C][/ROW]
[ROW][C]p-value[/C][C]0.425921163883119[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25921&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25921&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)
alpha22.9217115516362
beta-0.121099440332832
S.D.0.131789752830948
T-STAT-0.918883583370638
p-value0.425921163883119






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha8.84412341384336
beta-1.40196150324756
S.D.1.61681093319872
T-STAT-0.86711530362669
p-value0.449672351594223
Lambda2.40196150324756

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 8.84412341384336 \tabularnewline
beta & -1.40196150324756 \tabularnewline
S.D. & 1.61681093319872 \tabularnewline
T-STAT & -0.86711530362669 \tabularnewline
p-value & 0.449672351594223 \tabularnewline
Lambda & 2.40196150324756 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25921&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]8.84412341384336[/C][/ROW]
[ROW][C]beta[/C][C]-1.40196150324756[/C][/ROW]
[ROW][C]S.D.[/C][C]1.61681093319872[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.86711530362669[/C][/ROW]
[ROW][C]p-value[/C][C]0.449672351594223[/C][/ROW]
[ROW][C]Lambda[/C][C]2.40196150324756[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25921&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25921&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)
alpha8.84412341384336
beta-1.40196150324756
S.D.1.61681093319872
T-STAT-0.86711530362669
p-value0.449672351594223
Lambda2.40196150324756


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')