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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, 03 Dec 2010 17:18:37 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/03/t1291396625g3bi8hker8rzjyx.htm/, Retrieved Tue, 07 May 2024 07:08:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104936, Retrieved Tue, 07 May 2024 07:08:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106

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] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
-   PD      [Standard Deviation-Mean Plot] [ws9] [2010-12-03 17:18:37] [0cadca125c925bcc9e6efbdd1941e458] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10407
10463
10556
10646
10702
11353
11346
11451
11964
12574
13031
13812
14544
14931
14886
16005
17064
15168
16050
15839
15137
14954
15648
15305
15579
16348
15928
16171
15937
15713
15594
15683
16438
17032
17696
17745
19394
20148
20108
18584
18441
18391
19178
18079
18483
19644
19195
19650
20830
23595
22937
21814
21928
21777
21383
21467
22052
22680
24320
24977
25204
25739
26434
27525
30695
32436
30160
30236
31293
31077
32226

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'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 & 1 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104936&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104936&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104936&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
111525.41666666671112.953683454603405
215460.9166666667695.1090768993592520
316322775.6835109056462166
419107.9166666667703.2356980200142069
5224801260.966944992474147

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 11525.4166666667 & 1112.95368345460 & 3405 \tabularnewline
2 & 15460.9166666667 & 695.109076899359 & 2520 \tabularnewline
3 & 16322 & 775.683510905646 & 2166 \tabularnewline
4 & 19107.9166666667 & 703.235698020014 & 2069 \tabularnewline
5 & 22480 & 1260.96694499247 & 4147 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104936&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]11525.4166666667[/C][C]1112.95368345460[/C][C]3405[/C][/ROW]
[ROW][C]2[/C][C]15460.9166666667[/C][C]695.109076899359[/C][C]2520[/C][/ROW]
[ROW][C]3[/C][C]16322[/C][C]775.683510905646[/C][C]2166[/C][/ROW]
[ROW][C]4[/C][C]19107.9166666667[/C][C]703.235698020014[/C][C]2069[/C][/ROW]
[ROW][C]5[/C][C]22480[/C][C]1260.96694499247[/C][C]4147[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104936&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104936&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
111525.41666666671112.953683454603405
215460.9166666667695.1090768993592520
316322775.6835109056462166
419107.9166666667703.2356980200142069
5224801260.966944992474147






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha708.141649783817
beta0.0118643716931313
S.D.0.0360219045284788
T-STAT0.329365474936267
p-value0.763534845683853

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 708.141649783817 \tabularnewline
beta & 0.0118643716931313 \tabularnewline
S.D. & 0.0360219045284788 \tabularnewline
T-STAT & 0.329365474936267 \tabularnewline
p-value & 0.763534845683853 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104936&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]708.141649783817[/C][/ROW]
[ROW][C]beta[/C][C]0.0118643716931313[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0360219045284788[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.329365474936267[/C][/ROW]
[ROW][C]p-value[/C][C]0.763534845683853[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104936&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104936&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)
alpha708.141649783817
beta0.0118643716931313
S.D.0.0360219045284788
T-STAT0.329365474936267
p-value0.763534845683853






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha6.36407562665289
beta0.0429738141304855
S.D.0.638415854360397
T-STAT0.0673132000669676
p-value0.950567473280294
Lambda0.957026185869514

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 6.36407562665289 \tabularnewline
beta & 0.0429738141304855 \tabularnewline
S.D. & 0.638415854360397 \tabularnewline
T-STAT & 0.0673132000669676 \tabularnewline
p-value & 0.950567473280294 \tabularnewline
Lambda & 0.957026185869514 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104936&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]6.36407562665289[/C][/ROW]
[ROW][C]beta[/C][C]0.0429738141304855[/C][/ROW]
[ROW][C]S.D.[/C][C]0.638415854360397[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.0673132000669676[/C][/ROW]
[ROW][C]p-value[/C][C]0.950567473280294[/C][/ROW]
[ROW][C]Lambda[/C][C]0.957026185869514[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104936&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104936&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)
alpha6.36407562665289
beta0.0429738141304855
S.D.0.638415854360397
T-STAT0.0673132000669676
p-value0.950567473280294
Lambda0.957026185869514


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 2 ; par4 = 1 ;
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')