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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 computationTue, 09 Dec 2008 05:57:00 -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/09/t12288274531t6zqvqc2pvltv5.htm/, Retrieved Sun, 19 May 2024 10:48:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=31350, Retrieved Sun, 19 May 2024 10:48:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact211

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]
F RMPD    [Standard Deviation-Mean Plot] [Identification an...] [2008-12-09 12:57:00] [acca1d0ee7cc95ffc080d0867a313954] [Current]
F RM D      [Variance Reduction Matrix] [Identification an...] [2008-12-09 13:00:44] [8ac58ef7b35dc5a117bc162cf16850e9]
F RM          [(Partial) Autocorrelation Function] [Identification an...] [2008-12-09 13:03:20] [8ac58ef7b35dc5a117bc162cf16850e9]
F RM            [Spectral Analysis] [Identification an...] [2008-12-09 13:05:46] [8ac58ef7b35dc5a117bc162cf16850e9]
F RM              [(Partial) Autocorrelation Function] [Identification an...] [2008-12-09 13:10:48] [8ac58ef7b35dc5a117bc162cf16850e9]
- RMP               [ARIMA Backward Selection] [ARIMA workshop IP] [2008-12-09 16:55:50] [74be16979710d4c4e7c6647856088456]
F RMP               [ARIMA Backward Selection] [Identification an...] [2008-12-09 17:00:28] [74be16979710d4c4e7c6647856088456]
F                     [ARIMA Backward Selection] [step 5 ip] [2008-12-09 18:09:34] [74be16979710d4c4e7c6647856088456]
F   P               [(Partial) Autocorrelation Function] [step 4 ip] [2008-12-09 18:08:11] [74be16979710d4c4e7c6647856088456]
F   P             [Spectral Analysis] [step 2 cp ip] [2008-12-09 18:07:03] [74be16979710d4c4e7c6647856088456]
F   P           [(Partial) Autocorrelation Function] [step 2 acf1 ip] [2008-12-09 18:05:59] [74be16979710d4c4e7c6647856088456]
F   PD      [Standard Deviation-Mean Plot] [step 1 ip] [2008-12-09 18:02:49] [74be16979710d4c4e7c6647856088456]
- RMPD      [Variance Reduction Matrix] [step 2 vrm ip] [2008-12-09 18:04:33] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2008-12-15 15:31:05 [Jessica Alves Pires] [reply] 
Goede conclusie.De p-waarde is groter dan 5% dus mag lambda gelijk blijven aan 1.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
110,40
96,40
101,90
106,20
81,00
94,70
101,00
109,40
102,30
90,70
96,20
96,10
106,00
103,10
102,00
104,70
86,00
92,10
106,90
112,60
101,70
92,00
97,40
97,00
105,40
102,70
98,10
104,50
87,40
89,90
109,80
111,70
98,60
96,90
95,10
97,00
112,70
102,90
97,40
111,40
87,40
96,80
114,10
110,30
103,90
101,60
94,60
95,90
104,70
102,80
98,10
113,90
80,90
95,70
113,20
105,90
108,80
102,30
99,00
100,70
115,50

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=31350&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=31350&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31350&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
198.85833333333338.2429538321397829.4
2100.1257.5011665759400426.6
399.75833333333337.3724867314943724.3
4102.4166666666678.3881771053975226.7
5102.1666666666678.7748953201042733

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 98.8583333333333 & 8.24295383213978 & 29.4 \tabularnewline
2 & 100.125 & 7.50116657594004 & 26.6 \tabularnewline
3 & 99.7583333333333 & 7.37248673149437 & 24.3 \tabularnewline
4 & 102.416666666667 & 8.38817710539752 & 26.7 \tabularnewline
5 & 102.166666666667 & 8.77489532010427 & 33 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31350&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]98.8583333333333[/C][C]8.24295383213978[/C][C]29.4[/C][/ROW]
[ROW][C]2[/C][C]100.125[/C][C]7.50116657594004[/C][C]26.6[/C][/ROW]
[ROW][C]3[/C][C]99.7583333333333[/C][C]7.37248673149437[/C][C]24.3[/C][/ROW]
[ROW][C]4[/C][C]102.416666666667[/C][C]8.38817710539752[/C][C]26.7[/C][/ROW]
[ROW][C]5[/C][C]102.166666666667[/C][C]8.77489532010427[/C][C]33[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31350&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31350&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
198.85833333333338.2429538321397829.4
2100.1257.5011665759400426.6
399.75833333333337.3724867314943724.3
4102.4166666666678.3881771053975226.7
5102.1666666666678.7748953201042733






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-15.2187726488817
beta0.231209542163581
S.D.0.177655688113077
T-STAT1.30144744938545
p-value0.284032496741516

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -15.2187726488817 \tabularnewline
beta & 0.231209542163581 \tabularnewline
S.D. & 0.177655688113077 \tabularnewline
T-STAT & 1.30144744938545 \tabularnewline
p-value & 0.284032496741516 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31350&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-15.2187726488817[/C][/ROW]
[ROW][C]beta[/C][C]0.231209542163581[/C][/ROW]
[ROW][C]S.D.[/C][C]0.177655688113077[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.30144744938545[/C][/ROW]
[ROW][C]p-value[/C][C]0.284032496741516[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31350&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31350&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-15.2187726488817
beta0.231209542163581
S.D.0.177655688113077
T-STAT1.30144744938545
p-value0.284032496741516






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-11.1014718424780
beta2.85917180740622
S.D.2.25530307950629
T-STAT1.26775502298881
p-value0.294341125821297
Lambda-1.85917180740622

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -11.1014718424780 \tabularnewline
beta & 2.85917180740622 \tabularnewline
S.D. & 2.25530307950629 \tabularnewline
T-STAT & 1.26775502298881 \tabularnewline
p-value & 0.294341125821297 \tabularnewline
Lambda & -1.85917180740622 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31350&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-11.1014718424780[/C][/ROW]
[ROW][C]beta[/C][C]2.85917180740622[/C][/ROW]
[ROW][C]S.D.[/C][C]2.25530307950629[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.26775502298881[/C][/ROW]
[ROW][C]p-value[/C][C]0.294341125821297[/C][/ROW]
[ROW][C]Lambda[/C][C]-1.85917180740622[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31350&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31350&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-11.1014718424780
beta2.85917180740622
S.D.2.25530307950629
T-STAT1.26775502298881
p-value0.294341125821297
Lambda-1.85917180740622


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 0 ;
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')