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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, 08 Dec 2008 12:50:20 -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/08/t1228765898hvqc9ceg52yrl9g.htm/, Retrieved Thu, 16 May 2024 19:08:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=30870, Retrieved Thu, 16 May 2024 19:08:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact208
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]
F RMPD  [Cross Correlation Function] [Q7 - zonder trans...] [2008-12-01 20:04:13] [299afd6311e4c20059ea2f05c8dd029d]
-         [Cross Correlation Function] [Q7 - 2 ] [2008-12-01 20:11:27] [299afd6311e4c20059ea2f05c8dd029d]
- RM D        [Standard Deviation-Mean Plot] [Verbetering Q7] [2008-12-08 19:50:20] [5e2b1e7aa808f9f0d23fd35605d4968f] [Current]
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Dataseries X:
12192.5
11268.8
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




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=30870&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=30870&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30870&T=0

As an alternative you can also use a 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
111902.01666666671202.843261786114835.8
213184.64166666671100.623135450044184.5
313786.70833333331248.193374047134577
414576.00833333331116.297087744153581
516067.54166666671719.676986296255587.6

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 11902.0166666667 & 1202.84326178611 & 4835.8 \tabularnewline
2 & 13184.6416666667 & 1100.62313545004 & 4184.5 \tabularnewline
3 & 13786.7083333333 & 1248.19337404713 & 4577 \tabularnewline
4 & 14576.0083333333 & 1116.29708774415 & 3581 \tabularnewline
5 & 16067.5416666667 & 1719.67698629625 & 5587.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30870&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]11902.0166666667[/C][C]1202.84326178611[/C][C]4835.8[/C][/ROW]
[ROW][C]2[/C][C]13184.6416666667[/C][C]1100.62313545004[/C][C]4184.5[/C][/ROW]
[ROW][C]3[/C][C]13786.7083333333[/C][C]1248.19337404713[/C][C]4577[/C][/ROW]
[ROW][C]4[/C][C]14576.0083333333[/C][C]1116.29708774415[/C][C]3581[/C][/ROW]
[ROW][C]5[/C][C]16067.5416666667[/C][C]1719.67698629625[/C][C]5587.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30870&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30870&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
111902.01666666671202.843261786114835.8
213184.64166666671100.623135450044184.5
313786.70833333331248.193374047134577
414576.00833333331116.297087744153581
516067.54166666671719.676986296255587.6







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-344.697393898663
beta0.116678374182068
S.D.0.0663039050360337
T-STAT1.75975116576705
p-value0.176679995817998

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -344.697393898663 \tabularnewline
beta & 0.116678374182068 \tabularnewline
S.D. & 0.0663039050360337 \tabularnewline
T-STAT & 1.75975116576705 \tabularnewline
p-value & 0.176679995817998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30870&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-344.697393898663[/C][/ROW]
[ROW][C]beta[/C][C]0.116678374182068[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0663039050360337[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.75975116576705[/C][/ROW]
[ROW][C]p-value[/C][C]0.176679995817998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30870&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30870&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-344.697393898663
beta0.116678374182068
S.D.0.0663039050360337
T-STAT1.75975116576705
p-value0.176679995817998







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-3.09317455609954
beta1.07309254274058
S.D.0.703051146636706
T-STAT1.52633638089362
p-value0.224356493581621
Lambda-0.07309254274058

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -3.09317455609954 \tabularnewline
beta & 1.07309254274058 \tabularnewline
S.D. & 0.703051146636706 \tabularnewline
T-STAT & 1.52633638089362 \tabularnewline
p-value & 0.224356493581621 \tabularnewline
Lambda & -0.07309254274058 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30870&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-3.09317455609954[/C][/ROW]
[ROW][C]beta[/C][C]1.07309254274058[/C][/ROW]
[ROW][C]S.D.[/C][C]0.703051146636706[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.52633638089362[/C][/ROW]
[ROW][C]p-value[/C][C]0.224356493581621[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.07309254274058[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30870&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30870&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-3.09317455609954
beta1.07309254274058
S.D.0.703051146636706
T-STAT1.52633638089362
p-value0.224356493581621
Lambda-0.07309254274058



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')