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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 computationSun, 14 Dec 2008 08:48:50 -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/14/t12292697650yvxqypmfuy6wuz.htm/, Retrieved Wed, 15 May 2024 20:55:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=33440, Retrieved Wed, 15 May 2024 20:55:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact180

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Variance Reduction Matrix] [workshop] [2008-12-14 15:44:19] [3a9fc6d5b5e0e816787b7dbace57e7cd]
F RMP   [Spectral Analysis] [workshop] [2008-12-14 15:45:47] [3a9fc6d5b5e0e816787b7dbace57e7cd]
F   P     [Spectral Analysis] [workshop] [2008-12-14 15:47:26] [3a9fc6d5b5e0e816787b7dbace57e7cd]
F RMP         [Standard Deviation-Mean Plot] [workshop] [2008-12-14 15:48:50] [821c4b3d195be8e737cf8c9dc649d3cf] [Current]
F RMP           [(Partial) Autocorrelation Function] [workshop] [2008-12-14 15:50:28] [3a9fc6d5b5e0e816787b7dbace57e7cd]
F RMP             [ARIMA Backward Selection] [workshop] [2008-12-14 15:55:32] [3a9fc6d5b5e0e816787b7dbace57e7cd]
F RMP               [ARIMA Forecasting] [workshop] [2008-12-14 15:58:35] [3a9fc6d5b5e0e816787b7dbace57e7cd]
Feedback Forum
2008-12-24 07:28:27 [Gert-Jan Geudens] [reply] 
Correct, Lambda is hier door de te hoge p-waarde, inderdaad gelijk aan 1.
Voor meer informatie in verband met de standard-deviation mean plot verwijzen we graag naar de vorige workshops waar dit reeds uitvoerig is besproken.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2074
2049
2406
2558
2251
2059
2397
1747
1707
2319
1631
1627
1791
2034
1997
2169
2028
2253
2218
1855
2187
1852
1570
1851
1954
1828
2251
2277
2085
2282
2266
1878
2267
2069
1746
2299
2360
2214
2825
2355
2333
3016
2155
2172
2150
2533
2058
2160
2259
2498
2695
2799
2945
2930
2318
2540
2570
2669
2450
2842

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=33440&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=33440&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33440&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
12068.75327.297230774608931
21983.75206.167242349947683
32100.16666666667203.019404954006553
42360.91666666667294.065997024581958
52626.25226.598613892896686

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 2068.75 & 327.297230774608 & 931 \tabularnewline
2 & 1983.75 & 206.167242349947 & 683 \tabularnewline
3 & 2100.16666666667 & 203.019404954006 & 553 \tabularnewline
4 & 2360.91666666667 & 294.065997024581 & 958 \tabularnewline
5 & 2626.25 & 226.598613892896 & 686 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33440&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]2068.75[/C][C]327.297230774608[/C][C]931[/C][/ROW]
[ROW][C]2[/C][C]1983.75[/C][C]206.167242349947[/C][C]683[/C][/ROW]
[ROW][C]3[/C][C]2100.16666666667[/C][C]203.019404954006[/C][C]553[/C][/ROW]
[ROW][C]4[/C][C]2360.91666666667[/C][C]294.065997024581[/C][C]958[/C][/ROW]
[ROW][C]5[/C][C]2626.25[/C][C]226.598613892896[/C][C]686[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33440&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33440&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
12068.75327.297230774608931
21983.75206.167242349947683
32100.16666666667203.019404954006553
42360.91666666667294.065997024581958
52626.25226.598613892896686






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha243.886283837396
beta0.00338578403109495
S.D.0.122895487741969
T-STAT0.0275501085784674
p-value0.979751187227903

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 243.886283837396 \tabularnewline
beta & 0.00338578403109495 \tabularnewline
S.D. & 0.122895487741969 \tabularnewline
T-STAT & 0.0275501085784674 \tabularnewline
p-value & 0.979751187227903 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33440&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]243.886283837396[/C][/ROW]
[ROW][C]beta[/C][C]0.00338578403109495[/C][/ROW]
[ROW][C]S.D.[/C][C]0.122895487741969[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.0275501085784674[/C][/ROW]
[ROW][C]p-value[/C][C]0.979751187227903[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33440&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33440&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)
alpha243.886283837396
beta0.00338578403109495
S.D.0.122895487741969
T-STAT0.0275501085784674
p-value0.979751187227903






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha4.39985666725383
beta0.143845863208527
S.D.1.08749563757669
T-STAT0.132272588724186
p-value0.90314180732255
Lambda0.856154136791473

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 4.39985666725383 \tabularnewline
beta & 0.143845863208527 \tabularnewline
S.D. & 1.08749563757669 \tabularnewline
T-STAT & 0.132272588724186 \tabularnewline
p-value & 0.90314180732255 \tabularnewline
Lambda & 0.856154136791473 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33440&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]4.39985666725383[/C][/ROW]
[ROW][C]beta[/C][C]0.143845863208527[/C][/ROW]
[ROW][C]S.D.[/C][C]1.08749563757669[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.132272588724186[/C][/ROW]
[ROW][C]p-value[/C][C]0.90314180732255[/C][/ROW]
[ROW][C]Lambda[/C][C]0.856154136791473[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33440&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33440&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)
alpha4.39985666725383
beta0.143845863208527
S.D.1.08749563757669
T-STAT0.132272588724186
p-value0.90314180732255
Lambda0.856154136791473


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')