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Description of Statistical Computation

Author's title

koers euro in usd standard deviation mean plot Correctie SVEN VAN LAERE 2MA...

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationSat, 24 May 2008 06:11:09 -0600
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/May/24/t12116311903ikf5d6vsvo6s4k.htm/, Retrieved Mon, 20 May 2024 00:41:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=13061, Retrieved Mon, 20 May 2024 00:41:03 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact199

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Standard Deviation-Mean Plot] [koers euro in usd...] [2008-05-24 12:11:09] [9eefa1691b07b95ff73d963c22019b45] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1.1591
1.1203
1.0886
1.0701
1.0630
1.0377
1.0370
1.0606
1.0497
1.0706
1.0328
1.0110
1.0131
0.9834
0.9643
0.9449
0.9059
0.9505
0.9386
0.9045
0.8695
0.8525
0.8552
0.8983
0.9376
0.9205
0.9083
0.8925
0.8753
0.8530
0.8615
0.9014
0.9114
0.9050
0.8883
0.8912
0.8832
0.8706
0.8766
0.8860
0.9170
0.9561
0.9935
0.9781
0.9806
0.9812
1.0013
1.0194
1.0622
1.0785
1.0805
1.0862
1.1556
1.1674
1.1365
1.1155
1.1266
1.1714
1.1710
1.2298
1.2638
1.2640
1.2261
1.1989
1.2000
1.2146
1.2266
1.2191
1.2224
1.2507
1.2997
1.3406

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=13061&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=13061&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=13061&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
11.066708333333330.04071161233343480.1481
20.9233916666666670.05101098737853770.1606
30.89550.02409967936414390.0846
40.94530.05489742500998820.1488
51.131766666666670.04977541074819490.1676
61.2438750.04244237001779320.1417

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 1.06670833333333 & 0.0407116123334348 & 0.1481 \tabularnewline
2 & 0.923391666666667 & 0.0510109873785377 & 0.1606 \tabularnewline
3 & 0.8955 & 0.0240996793641439 & 0.0846 \tabularnewline
4 & 0.9453 & 0.0548974250099882 & 0.1488 \tabularnewline
5 & 1.13176666666667 & 0.0497754107481949 & 0.1676 \tabularnewline
6 & 1.243875 & 0.0424423700177932 & 0.1417 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=13061&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]1.06670833333333[/C][C]0.0407116123334348[/C][C]0.1481[/C][/ROW]
[ROW][C]2[/C][C]0.923391666666667[/C][C]0.0510109873785377[/C][C]0.1606[/C][/ROW]
[ROW][C]3[/C][C]0.8955[/C][C]0.0240996793641439[/C][C]0.0846[/C][/ROW]
[ROW][C]4[/C][C]0.9453[/C][C]0.0548974250099882[/C][C]0.1488[/C][/ROW]
[ROW][C]5[/C][C]1.13176666666667[/C][C]0.0497754107481949[/C][C]0.1676[/C][/ROW]
[ROW][C]6[/C][C]1.243875[/C][C]0.0424423700177932[/C][C]0.1417[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=13061&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=13061&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
11.066708333333330.04071161233343480.1481
20.9233916666666670.05101098737853770.1606
30.89550.02409967936414390.0846
40.94530.05489742500998820.1488
51.131766666666670.04977541074819490.1676
61.2438750.04244237001779320.1417






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha0.0312200100235166
beta0.0121835039176664
S.D.0.0398351780753671
T-STAT0.3058478587598
p-value0.774977317783074

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 0.0312200100235166 \tabularnewline
beta & 0.0121835039176664 \tabularnewline
S.D. & 0.0398351780753671 \tabularnewline
T-STAT & 0.3058478587598 \tabularnewline
p-value & 0.774977317783074 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=13061&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.0312200100235166[/C][/ROW]
[ROW][C]beta[/C][C]0.0121835039176664[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0398351780753671[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.3058478587598[/C][/ROW]
[ROW][C]p-value[/C][C]0.774977317783074[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=13061&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=13061&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)
alpha0.0312200100235166
beta0.0121835039176664
S.D.0.0398351780753671
T-STAT0.3058478587598
p-value0.774977317783074






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-3.17674902768463
beta0.598087292755375
S.D.1.11078795292358
T-STAT0.538435163238148
p-value0.618841706986806
Lambda0.401912707244625

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -3.17674902768463 \tabularnewline
beta & 0.598087292755375 \tabularnewline
S.D. & 1.11078795292358 \tabularnewline
T-STAT & 0.538435163238148 \tabularnewline
p-value & 0.618841706986806 \tabularnewline
Lambda & 0.401912707244625 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=13061&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-3.17674902768463[/C][/ROW]
[ROW][C]beta[/C][C]0.598087292755375[/C][/ROW]
[ROW][C]S.D.[/C][C]1.11078795292358[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.538435163238148[/C][/ROW]
[ROW][C]p-value[/C][C]0.618841706986806[/C][/ROW]
[ROW][C]Lambda[/C][C]0.401912707244625[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=13061&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=13061&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-3.17674902768463
beta0.598087292755375
S.D.1.11078795292358
T-STAT0.538435163238148
p-value0.618841706986806
Lambda0.401912707244625


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')