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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 computationMon, 01 Dec 2008 12:27:47 -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/01/t1228159682cbo4ztf02w6sr0g.htm/, Retrieved Sun, 05 May 2024 13:53:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27228, Retrieved Sun, 05 May 2024 13:53:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact293

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
- RMPD  [Standard Deviation-Mean Plot] [Q5] [2008-11-29 20:10:39] [57fa5e3679c393aa19449b2f1be9928b]
-   P     [Standard Deviation-Mean Plot] [Q5] [2008-11-29 20:18:39] [57fa5e3679c393aa19449b2f1be9928b]
- RM        [Variance Reduction Matrix] [Q6 Variance] [2008-11-29 20:25:29] [57fa5e3679c393aa19449b2f1be9928b]
- RM          [(Partial) Autocorrelation Function] [Q6 ACF] [2008-11-29 20:35:57] [57fa5e3679c393aa19449b2f1be9928b]
-               [(Partial) Autocorrelation Function] [Q6 aangepaste ACF] [2008-11-29 20:44:03] [57fa5e3679c393aa19449b2f1be9928b]
- RM D            [Cross Correlation Function] [Q7] [2008-11-29 20:55:14] [57fa5e3679c393aa19449b2f1be9928b]
F RM D              [Standard Deviation-Mean Plot] [Q8 Mean plot insc...] [2008-11-29 21:03:17] [57fa5e3679c393aa19449b2f1be9928b]
-   P                 [Standard Deviation-Mean Plot] [] [2008-11-30 11:21:31] [a4ee3bef49b119f4bd2e925060c84f5e]
F    D                    [Standard Deviation-Mean Plot] [] [2008-12-01 19:27:47] [db9a5fd0f9c3e1245d8075d8bb09236d] [Current]
Feedback Forum
2008-12-07 14:05:34 [Stijn Van de Velde] [reply] 
Niet volledig. De lambda berekening is juist.

Zie Q3: op deze manier kan je te weten komen hoeveel keer we d en D zouden moeten transformeren.

d=0 wil zeggen dat er geen lange termijn trend is
D=0 wil zeggen dat er geen seizoenaliteit is.

Als er wel 1 van de 2 vorige is, gaan we d en/of D op 1, 2 of 3 zetten (afhankelijk van welke orde de trend/seizoenaliteit is) om zo de trend of seizoenaliteit er uit te halen. Op die manier word de tijdreeks stationair gemaakt. Dit noemt men differentiëren.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9097,4
12639,8
13040,1
11687,3
11191,7
11391,9
11793,1
13933,2
12778,1
11810,3
13698,4
11956,6
10723,8
13938,9
13979,8
13807,4
12973,9
12509,8
12934,1
14908,3
13772,1
13012,6
14049,9
11816,5
11593,2
14466,2
13615,9
14733,9
13880,7
13527,5
13584
16170,2
13260,6
14741,9
15486,5
13154,5
12621,2
15031,6
15452,4
15428
13105,9
14716,8
14180
16202,2
14392,4
15140,6
15960,1
14351,3
13230,2
15202,1
17157,3
16159,1
13405,7
17224,7
17338,4
17370,6
18817,8
16593,2
17979,5
17015,2

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=27228&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=27228&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27228&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
112084.8251288.365874858544835.8
213202.25833333331134.973352877694184.5
314017.9251199.784385269444577
414715.20833333331069.890136635393581
516457.81666666671713.555984088665587.6

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 12084.825 & 1288.36587485854 & 4835.8 \tabularnewline
2 & 13202.2583333333 & 1134.97335287769 & 4184.5 \tabularnewline
3 & 14017.925 & 1199.78438526944 & 4577 \tabularnewline
4 & 14715.2083333333 & 1069.89013663539 & 3581 \tabularnewline
5 & 16457.8166666667 & 1713.55598408866 & 5587.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27228&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]12084.825[/C][C]1288.36587485854[/C][C]4835.8[/C][/ROW]
[ROW][C]2[/C][C]13202.2583333333[/C][C]1134.97335287769[/C][C]4184.5[/C][/ROW]
[ROW][C]3[/C][C]14017.925[/C][C]1199.78438526944[/C][C]4577[/C][/ROW]
[ROW][C]4[/C][C]14715.2083333333[/C][C]1069.89013663539[/C][C]3581[/C][/ROW]
[ROW][C]5[/C][C]16457.8166666667[/C][C]1713.55598408866[/C][C]5587.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27228&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27228&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
112084.8251288.365874858544835.8
213202.25833333331134.973352877694184.5
314017.9251199.784385269444577
414715.20833333331069.890136635393581
516457.81666666671713.555984088665587.6






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-39.3355970884794
beta0.0936922812238723
S.D.0.0712692209887278
T-STAT1.31462474156538
p-value0.280104213640506

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -39.3355970884794 \tabularnewline
beta & 0.0936922812238723 \tabularnewline
S.D. & 0.0712692209887278 \tabularnewline
T-STAT & 1.31462474156538 \tabularnewline
p-value & 0.280104213640506 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27228&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-39.3355970884794[/C][/ROW]
[ROW][C]beta[/C][C]0.0936922812238723[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0712692209887278[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.31462474156538[/C][/ROW]
[ROW][C]p-value[/C][C]0.280104213640506[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27228&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27228&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-39.3355970884794
beta0.0936922812238723
S.D.0.0712692209887278
T-STAT1.31462474156538
p-value0.280104213640506






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-0.742800675652445
beta0.82572467474791
S.D.0.781561447593096
T-STAT1.05650640431513
p-value0.368290682100482
Lambda0.17427532525209

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -0.742800675652445 \tabularnewline
beta & 0.82572467474791 \tabularnewline
S.D. & 0.781561447593096 \tabularnewline
T-STAT & 1.05650640431513 \tabularnewline
p-value & 0.368290682100482 \tabularnewline
Lambda & 0.17427532525209 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27228&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.742800675652445[/C][/ROW]
[ROW][C]beta[/C][C]0.82572467474791[/C][/ROW]
[ROW][C]S.D.[/C][C]0.781561447593096[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.05650640431513[/C][/ROW]
[ROW][C]p-value[/C][C]0.368290682100482[/C][/ROW]
[ROW][C]Lambda[/C][C]0.17427532525209[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27228&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27228&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-0.742800675652445
beta0.82572467474791
S.D.0.781561447593096
T-STAT1.05650640431513
p-value0.368290682100482
Lambda0.17427532525209


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;
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')