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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 computationTue, 02 Dec 2008 14:23:44 -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/02/t122825315047rt2441xadwjuq.htm/, Retrieved Sun, 19 May 2024 10:10:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28472, Retrieved Sun, 19 May 2024 10:10:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact188

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Spectral Analysis] [Q6 - 3e methode -...] [2008-12-02 20:05:14] [7a664918911e34206ce9d0436dd7c1c8]
- RMPD  [Variance Reduction Matrix] [Q8 - eerste reeks...] [2008-12-02 21:03:48] [7a664918911e34206ce9d0436dd7c1c8]
F RM D      [Standard Deviation-Mean Plot] [Q8 - tweede reeks...] [2008-12-02 21:23:44] [98255691c21504803b38711776845ae0] [Current]
Feedback Forum
2008-12-06 14:10:37 [Natalie De Wilde] [reply] 
Zeer goed. Cross correlation geeft het verband tussen twee reeksen op dynamische waarden. We voorspellen Y(t) aan de hand van X(t) of het verleden van X(t).
Wanneer k=0 heeft er geen verschuiving in de tijd plaatsgevonden.
Bij k groter dan 0 wordt de toekomstige waarde van X(t) gecorreleerd aan de waarde van Y(t). Bij k kleiner dan 0 wordt de verleden waarde van X(t) gecorreleerd aan de waarde van Y(t).
X(t) is leading indicator voor Y(t), deze geeft op voorhand informatie over andere variabelen.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-19
0
-18
-17
0
-16
-1
-11
7
-2
-9
-8
4
-7
2
1
3
5
0
11
5
-1
17
0
4
13
0
-7
19
9
2
9
8
-2
-6
-5
11

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=28472&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=28472&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28472&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
1-7.833333333333338.6005637588298226
23.333333333333336.0802711263312124
33.666666666666678.1389449096565526

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & -7.83333333333333 & 8.60056375882982 & 26 \tabularnewline
2 & 3.33333333333333 & 6.08027112633121 & 24 \tabularnewline
3 & 3.66666666666667 & 8.13894490965655 & 26 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28472&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]-7.83333333333333[/C][C]8.60056375882982[/C][C]26[/C][/ROW]
[ROW][C]2[/C][C]3.33333333333333[/C][C]6.08027112633121[/C][C]24[/C][/ROW]
[ROW][C]3[/C][C]3.66666666666667[/C][C]8.13894490965655[/C][C]26[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28472&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28472&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
1-7.833333333333338.6005637588298226
23.333333333333336.0802711263312124
33.666666666666678.1389449096565526






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha7.57118624105696
beta-0.127465285976040
S.D.0.160558222327947
T-STAT-0.793888248934938
p-value0.572826134755491

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 7.57118624105696 \tabularnewline
beta & -0.127465285976040 \tabularnewline
S.D. & 0.160558222327947 \tabularnewline
T-STAT & -0.793888248934938 \tabularnewline
p-value & 0.572826134755491 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28472&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]7.57118624105696[/C][/ROW]
[ROW][C]beta[/C][C]-0.127465285976040[/C][/ROW]
[ROW][C]S.D.[/C][C]0.160558222327947[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.793888248934938[/C][/ROW]
[ROW][C]p-value[/C][C]0.572826134755491[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28472&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28472&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)
alpha7.57118624105696
beta-0.127465285976040
S.D.0.160558222327947
T-STAT-0.793888248934938
p-value0.572826134755491






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-1.87862892781043
beta3.05960251153946
S.D.NaN
T-STATNaN
p-valueNaN
Lambda-2.05960251153946

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -1.87862892781043 \tabularnewline
beta & 3.05960251153946 \tabularnewline
S.D. & NaN \tabularnewline
T-STAT & NaN \tabularnewline
p-value & NaN \tabularnewline
Lambda & -2.05960251153946 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28472&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-1.87862892781043[/C][/ROW]
[ROW][C]beta[/C][C]3.05960251153946[/C][/ROW]
[ROW][C]S.D.[/C][C]NaN[/C][/ROW]
[ROW][C]T-STAT[/C][C]NaN[/C][/ROW]
[ROW][C]p-value[/C][C]NaN[/C][/ROW]
[ROW][C]Lambda[/C][C]-2.05960251153946[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28472&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28472&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-1.87862892781043
beta3.05960251153946
S.D.NaN
T-STATNaN
p-valueNaN
Lambda-2.05960251153946


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')