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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, 05 Dec 2008 11:06:39 -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/05/t1228500446iomiiragot33v62.htm/, Retrieved Thu, 16 May 2024 10:12:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=29373, Retrieved Thu, 16 May 2024 10:12:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact198

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] [step 2 uitvoer] [2008-12-05 17:47:26] [3a9fc6d5b5e0e816787b7dbace57e7cd]
F RM D    [Standard Deviation-Mean Plot] [step 3 uitvoer] [2008-12-05 18:06:39] [821c4b3d195be8e737cf8c9dc649d3cf] [Current]
- RMP       [(Partial) Autocorrelation Function] [acf lambda -0.3] [2008-12-05 18:09:06] [3a9fc6d5b5e0e816787b7dbace57e7cd]
- RMP         [ARIMA Backward Selection] [step 5 uitvoer] [2008-12-05 18:58:55] [3a9fc6d5b5e0e816787b7dbace57e7cd]
Feedback Forum
2008-12-14 20:42:46 [Vincent Vanden Poel] [reply] 
Correct uitgevoerd. Aan de hand van de eerste tabel gaan we na of het nodig is om een transformatie door te voeren. Indien dit het geval is moeten we kijken naar de lambda waarde in de 2e tabel. De optimale lambda is hier -0,3. We moeten er echter ook op letten dat de p-value hier onder 0,05 blijft. Hier is dit inderdaad het geval. Moest dit niet zo zijn is de lambda niet nodig en moet deze gelijk blijven aan 0.
2008-12-15 15:19:03 [Natalie De Wilde] [reply] 
Goed. De lambda waarde mag gebruikt worden voor verdere transformaties. Er is een consistent positief verband tussen de spreiding en het gemiddelde en de p-waarde is kleiner dan 5%. Dit zijn de twee voorwaarden waaraan voldaan moet worden.
2008-12-15 16:09:51 [Vincent Dolhain] [reply] 
correct
2008-12-16 19:05:15 [Gert-Jan Geudens] [reply] 
Correct. je mag deze lambda toepassen aangezien de p-waarde zeer klein is.
2008-12-16 20:07:54 [Gert-Jan Geudens] [reply] 
CORRECTIE VAN ONZE VORIGE FEEDBACKS MET BETREKKING TOT DE STEP 1 VAN DE EIGEN GEKOZEN TIJDREEKSEN :

Als er in de standard-deviation mean plot nog geen punt linksboven staat, en we zouden er hier 1 toevoegen, kan dit de regressielijn sterk beinvloeden. Om te concluderen of de transformatie nuttig is, volstaat het om naar de p-waarden te kijken. We hoeven ons dus niet op deze regressielijn te concentreren voor deze conclusie.

Onze excuses hiervoor.
  2008-12-16 20:18:34 [Gert-Jan Geudens] [reply] 
Onwille van mogelijke onduidelijkheid zullen we de feedback op step 1 van de eigen tijdreeksen nog eens kort herhalen.

Als de p-waarde hoger is dan 0.05 is de transformatie nutteloos. We kunnen tevens naar de standard-deviation mean plot kijken of er al dan niet een verband is tussen de gegevens.

Als we -indien er nog geen punt staat- linksboven een punt zouden toevoegen , dan zal dit de regressierechte sterk kunnen beïnvloeden.

Nogmaals onze oprechte excuses hiervoor

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2150.3
2425.7
2642.0
2291.5
2570.7
2526.6
2266.2
1981.9
2630.3
2942.6
2713.4
2437.5
2678.9
2582.0
2780.0
2512.4
2658.4
2708.7
2518.7
2018.3
2579.3
2693.5
2468.8
2122.8
2412.8
2370.6
2642.5
2634.2
2457.5
2579.1
2505.9
1903.2
2660.2
2844.1
2607.1
2356.0
2659.9
2531.4
2845.7
2654.3
2588.2
2789.6
2533.1
1846.5
2796.3
2895.6
2472.2
2584.4
2630.4
2663.1
3176.2
2856.7
2551.4
3088.7
2628.3
2226.2
3023.6
3077.9
3084.1
2990.3
2949.6
3014.7
3517.7
3121.2
3067.4
3174.6
2676.3
2424.0
3195.1
3146.6
3506.7
3528.5

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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29373&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29373&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29373&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'Gwilym Jenkins' @ 72.249.127.135






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
12464.89166666667264.191812062006960.7
22526.81666666667232.900333085883761.7
32497.76666666667234.161484036253940.9
42599.76666666667273.4335230273561049.1
52833.075290.320963840806950
63110.2331.2413045170871104.5

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 2464.89166666667 & 264.191812062006 & 960.7 \tabularnewline
2 & 2526.81666666667 & 232.900333085883 & 761.7 \tabularnewline
3 & 2497.76666666667 & 234.161484036253 & 940.9 \tabularnewline
4 & 2599.76666666667 & 273.433523027356 & 1049.1 \tabularnewline
5 & 2833.075 & 290.320963840806 & 950 \tabularnewline
6 & 3110.2 & 331.241304517087 & 1104.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29373&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]2464.89166666667[/C][C]264.191812062006[/C][C]960.7[/C][/ROW]
[ROW][C]2[/C][C]2526.81666666667[/C][C]232.900333085883[/C][C]761.7[/C][/ROW]
[ROW][C]3[/C][C]2497.76666666667[/C][C]234.161484036253[/C][C]940.9[/C][/ROW]
[ROW][C]4[/C][C]2599.76666666667[/C][C]273.433523027356[/C][C]1049.1[/C][/ROW]
[ROW][C]5[/C][C]2833.075[/C][C]290.320963840806[/C][C]950[/C][/ROW]
[ROW][C]6[/C][C]3110.2[/C][C]331.241304517087[/C][C]1104.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29373&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29373&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
12464.89166666667264.191812062006960.7
22526.81666666667232.900333085883761.7
32497.76666666667234.161484036253940.9
42599.76666666667273.4335230273561049.1
52833.075290.320963840806950
63110.2331.2413045170871104.5






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-88.2593741248434
beta0.134464582831253
S.D.0.0297279497377540
T-STAT4.52317041765197
p-value0.0106325690370336

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -88.2593741248434 \tabularnewline
beta & 0.134464582831253 \tabularnewline
S.D. & 0.0297279497377540 \tabularnewline
T-STAT & 4.52317041765197 \tabularnewline
p-value & 0.0106325690370336 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29373&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-88.2593741248434[/C][/ROW]
[ROW][C]beta[/C][C]0.134464582831253[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0297279497377540[/C][/ROW]
[ROW][C]T-STAT[/C][C]4.52317041765197[/C][/ROW]
[ROW][C]p-value[/C][C]0.0106325690370336[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29373&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29373&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-88.2593741248434
beta0.134464582831253
S.D.0.0297279497377540
T-STAT4.52317041765197
p-value0.0106325690370336






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-4.79908789797356
beta1.31782357797760
S.D.0.329331058268063
T-STAT4.001516239944
p-value0.0161097628196739
Lambda-0.317823577977599

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -4.79908789797356 \tabularnewline
beta & 1.31782357797760 \tabularnewline
S.D. & 0.329331058268063 \tabularnewline
T-STAT & 4.001516239944 \tabularnewline
p-value & 0.0161097628196739 \tabularnewline
Lambda & -0.317823577977599 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29373&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-4.79908789797356[/C][/ROW]
[ROW][C]beta[/C][C]1.31782357797760[/C][/ROW]
[ROW][C]S.D.[/C][C]0.329331058268063[/C][/ROW]
[ROW][C]T-STAT[/C][C]4.001516239944[/C][/ROW]
[ROW][C]p-value[/C][C]0.0161097628196739[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.317823577977599[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29373&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29373&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-4.79908789797356
beta1.31782357797760
S.D.0.329331058268063
T-STAT4.001516239944
p-value0.0161097628196739
Lambda-0.317823577977599


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')