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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 computationWed, 29 Dec 2010 11:00:54 +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/29/t1293620328etnbftagjixp6fg.htm/, Retrieved Fri, 03 May 2024 10:25:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116701, Retrieved Fri, 03 May 2024 10:25:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148

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] [] [2008-12-14 11:54:22] [d2d412c7f4d35ffbf5ee5ee89db327d4]
- RMP   [(Partial) Autocorrelation Function] [Workshop 9: ACF b...] [2010-12-17 15:33:01] [a48e3f697f1471e9c9650f8bf805cc06]
-   PD    [(Partial) Autocorrelation Function] [Paper: ACF (basis...] [2010-12-29 09:50:16] [a48e3f697f1471e9c9650f8bf805cc06]
- RMP         [Standard Deviation-Mean Plot] [Paper: SDM-plot (...] [2010-12-29 11:00:54] [35c3410767ea63f72c8afa35bf7b6164] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3065
2997
2901
2815
2709
2711
3509
3369
3596
3448
3160
2934
2534
2266
2088
1932
1784
1851
2700
2580
2829
2298
2045
1824
1872
1801
1735
1639
1521
1758
2603
2540
3103
2801
2590
2324
2424
2288
2163
2082
1937
2155
2874
2836
3439
3278
3129
2959
3060
2898
2783
2632
2465
2689
3321
3359
4108
3407
3241
3013
3067
2965
2823
2718
2567
2658
3436
3375
3931
3371
3038

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time27 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 & 27 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116701&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]27 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=116701&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116701&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 time27 seconds
R Server'George Udny Yule' @ 72.249.76.132






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
13101.16666666667312.579250557019887
22227.58333333333363.1995340238631045
32190.58333333333528.7349324452461582
42630.33333333333514.981435381781502
53081.33333333333445.5401896642551643

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 3101.16666666667 & 312.579250557019 & 887 \tabularnewline
2 & 2227.58333333333 & 363.199534023863 & 1045 \tabularnewline
3 & 2190.58333333333 & 528.734932445246 & 1582 \tabularnewline
4 & 2630.33333333333 & 514.98143538178 & 1502 \tabularnewline
5 & 3081.33333333333 & 445.540189664255 & 1643 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116701&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]3101.16666666667[/C][C]312.579250557019[/C][C]887[/C][/ROW]
[ROW][C]2[/C][C]2227.58333333333[/C][C]363.199534023863[/C][C]1045[/C][/ROW]
[ROW][C]3[/C][C]2190.58333333333[/C][C]528.734932445246[/C][C]1582[/C][/ROW]
[ROW][C]4[/C][C]2630.33333333333[/C][C]514.98143538178[/C][C]1502[/C][/ROW]
[ROW][C]5[/C][C]3081.33333333333[/C][C]445.540189664255[/C][C]1643[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116701&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116701&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
13101.16666666667312.579250557019887
22227.58333333333363.1995340238631045
32190.58333333333528.7349324452461582
42630.33333333333514.981435381781502
53081.33333333333445.5401896642551643






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha653.792556415239
beta-0.0834349210191241
S.D.0.113252992921104
T-STAT-0.73671272491004
p-value0.51467988663813

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 653.792556415239 \tabularnewline
beta & -0.0834349210191241 \tabularnewline
S.D. & 0.113252992921104 \tabularnewline
T-STAT & -0.73671272491004 \tabularnewline
p-value & 0.51467988663813 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116701&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]653.792556415239[/C][/ROW]
[ROW][C]beta[/C][C]-0.0834349210191241[/C][/ROW]
[ROW][C]S.D.[/C][C]0.113252992921104[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.73671272491004[/C][/ROW]
[ROW][C]p-value[/C][C]0.51467988663813[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116701&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116701&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)
alpha653.792556415239
beta-0.0834349210191241
S.D.0.113252992921104
T-STAT-0.73671272491004
p-value0.51467988663813






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha10.0162679059241
beta-0.503902782416693
S.D.0.722553841689293
T-STAT-0.697391326905957
p-value0.535745451303477
Lambda1.50390278241669

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 10.0162679059241 \tabularnewline
beta & -0.503902782416693 \tabularnewline
S.D. & 0.722553841689293 \tabularnewline
T-STAT & -0.697391326905957 \tabularnewline
p-value & 0.535745451303477 \tabularnewline
Lambda & 1.50390278241669 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116701&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]10.0162679059241[/C][/ROW]
[ROW][C]beta[/C][C]-0.503902782416693[/C][/ROW]
[ROW][C]S.D.[/C][C]0.722553841689293[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.697391326905957[/C][/ROW]
[ROW][C]p-value[/C][C]0.535745451303477[/C][/ROW]
[ROW][C]Lambda[/C][C]1.50390278241669[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116701&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116701&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)
alpha10.0162679059241
beta-0.503902782416693
S.D.0.722553841689293
T-STAT-0.697391326905957
p-value0.535745451303477
Lambda1.50390278241669


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')