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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 computationSun, 07 Dec 2008 02:34:56 -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/07/t1228643046kc1ubvxva0x5m1z.htm/, Retrieved Sun, 19 May 2024 09:23:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=29832, Retrieved Sun, 19 May 2024 09:23:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact216

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] [SMP Investeringsg...] [2008-12-07 09:34:56] [d41d8cd98f00b204e9800998ecf8427e] [Current]
F RMP       [(Partial) Autocorrelation Function] [ACF investeringsg...] [2008-12-09 17:07:54] [74be16979710d4c4e7c6647856088456]
F RMP       [(Partial) Autocorrelation Function] [ACF investeringsg...] [2008-12-09 17:48:15] [74be16979710d4c4e7c6647856088456]
- RMP         [ARIMA Backward Selection] [ARIMA investering...] [2008-12-09 18:45:00] [74be16979710d4c4e7c6647856088456]
F RMP       [Spectral Analysis] [Spectrum invester...] [2008-12-09 17:53:28] [74be16979710d4c4e7c6647856088456]
F RMP       [Spectral Analysis] [Spectrum invester...] [2008-12-09 18:03:43] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2008-12-14 19:44:13 [Steven Vercammen] [reply] 
Dit heb ik correct toegepast

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
93.4
101.5
110.4
105.9
108.4
113.9
86.1
69.4
101.2
100.5
98.0
106.6
90.1
96.9
125.9
112.0
100.0
123.9
79.8
83.4
113.6
112.9
104.0
109.9
99.0
106.3
128.9
111.1
102.9
130.0
87.0
87.5
117.6
103.4
110.8
112.6
102.5
112.4
135.6
105.1
127.7
137.0
91.0
90.5
122.4
123.3
124.3
120.0
118.1
119.0
142.7
123.6
129.6
151.6
110.4
99.2
130.5
136.2
129.7
128.0
121.6
135.8
143.8
147.5
136.2
156.6
123.3
104.5
143.6

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29832&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29832&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29832&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
199.608333333333312.171237207100844.5
2104.36666666666714.813405067605846.1
3108.09166666666713.630811243338243
4115.98333333333315.755104992774046.5
5126.5514.027019381439352.4

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 99.6083333333333 & 12.1712372071008 & 44.5 \tabularnewline
2 & 104.366666666667 & 14.8134050676058 & 46.1 \tabularnewline
3 & 108.091666666667 & 13.6308112433382 & 43 \tabularnewline
4 & 115.983333333333 & 15.7551049927740 & 46.5 \tabularnewline
5 & 126.55 & 14.0270193814393 & 52.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29832&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]99.6083333333333[/C][C]12.1712372071008[/C][C]44.5[/C][/ROW]
[ROW][C]2[/C][C]104.366666666667[/C][C]14.8134050676058[/C][C]46.1[/C][/ROW]
[ROW][C]3[/C][C]108.091666666667[/C][C]13.6308112433382[/C][C]43[/C][/ROW]
[ROW][C]4[/C][C]115.983333333333[/C][C]15.7551049927740[/C][C]46.5[/C][/ROW]
[ROW][C]5[/C][C]126.55[/C][C]14.0270193814393[/C][C]52.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29832&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29832&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
199.608333333333312.171237207100844.5
2104.36666666666714.813405067605846.1
3108.09166666666713.630811243338243
4115.98333333333315.755104992774046.5
5126.5514.027019381439352.4






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha7.72605999615193
beta0.0572796211891426
S.D.0.0651725333601848
T-STAT0.878892046018512
p-value0.444167458320269

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 7.72605999615193 \tabularnewline
beta & 0.0572796211891426 \tabularnewline
S.D. & 0.0651725333601848 \tabularnewline
T-STAT & 0.878892046018512 \tabularnewline
p-value & 0.444167458320269 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29832&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]7.72605999615193[/C][/ROW]
[ROW][C]beta[/C][C]0.0572796211891426[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0651725333601848[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.878892046018512[/C][/ROW]
[ROW][C]p-value[/C][C]0.444167458320269[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29832&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29832&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)
alpha7.72605999615193
beta0.0572796211891426
S.D.0.0651725333601848
T-STAT0.878892046018512
p-value0.444167458320269






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha0.256048455902398
beta0.506874820305338
S.D.0.517972675291589
T-STAT0.978574439317665
p-value0.399956859141918
Lambda0.493125179694662

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 0.256048455902398 \tabularnewline
beta & 0.506874820305338 \tabularnewline
S.D. & 0.517972675291589 \tabularnewline
T-STAT & 0.978574439317665 \tabularnewline
p-value & 0.399956859141918 \tabularnewline
Lambda & 0.493125179694662 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29832&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.256048455902398[/C][/ROW]
[ROW][C]beta[/C][C]0.506874820305338[/C][/ROW]
[ROW][C]S.D.[/C][C]0.517972675291589[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.978574439317665[/C][/ROW]
[ROW][C]p-value[/C][C]0.399956859141918[/C][/ROW]
[ROW][C]Lambda[/C][C]0.493125179694662[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29832&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29832&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)
alpha0.256048455902398
beta0.506874820305338
S.D.0.517972675291589
T-STAT0.978574439317665
p-value0.399956859141918
Lambda0.493125179694662


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')