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Description of Statistical Computation

Author's title

Spreidings- en gemiddeldegrafieken Index Consumptieprijzen Visitekaartjes b...

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationMon, 23 Apr 2012 10:22:21 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Apr/23/t1335190973x4me7j4yqqzepdi.htm/, Retrieved Sat, 04 May 2024 00:45:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=164709, Retrieved Sat, 04 May 2024 00:45:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact95

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] [Spreidings- en ge...] [2012-04-23 14:22:21] [b1a32f872c4b465525fe03c124440f0d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101,15
101,14
101,23
101,11
101,55
101,55
101,55
101,6
101,71
101,81
101,95
102,12
102,11
102,25
102,35
102,42
102,34
102,32
102,39
102,45
102,68
102,77
102,83
102,83
103,21
103,58
102,5
102,68
102,7
102,7
102,73
102,72
102,71
102,91
103,1
103,1
103,39
103,38
103,34
103,33
103,33
103,33
103,48
104,38
105,76
107,37
108,16
111,21
112,77
114,39
114,37
114,52
114,54
114,78
114,83
115,86
117
117,27
117,38
117,83

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'Gertrude Mary Cox' @ cox.wessa.net

\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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164709&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164709&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164709&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'Gertrude Mary Cox' @ cox.wessa.net






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
1101.5391666666670.3307418200275731.01000000000001
2102.4783333333330.2399936867856520.719999999999999
3102.8866666666670.3048496897379621.08
4105.0383333333332.589492169706197.88
5115.4616666666671.574494627915045.06

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 101.539166666667 & 0.330741820027573 & 1.01000000000001 \tabularnewline
2 & 102.478333333333 & 0.239993686785652 & 0.719999999999999 \tabularnewline
3 & 102.886666666667 & 0.304849689737962 & 1.08 \tabularnewline
4 & 105.038333333333 & 2.58949216970619 & 7.88 \tabularnewline
5 & 115.461666666667 & 1.57449462791504 & 5.06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164709&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]101.539166666667[/C][C]0.330741820027573[/C][C]1.01000000000001[/C][/ROW]
[ROW][C]2[/C][C]102.478333333333[/C][C]0.239993686785652[/C][C]0.719999999999999[/C][/ROW]
[ROW][C]3[/C][C]102.886666666667[/C][C]0.304849689737962[/C][C]1.08[/C][/ROW]
[ROW][C]4[/C][C]105.038333333333[/C][C]2.58949216970619[/C][C]7.88[/C][/ROW]
[ROW][C]5[/C][C]115.461666666667[/C][C]1.57449462791504[/C][C]5.06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164709&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164709&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
1101.5391666666670.3307418200275731.01000000000001
2102.4783333333330.2399936867856520.719999999999999
3102.8866666666670.3048496897379621.08
4105.0383333333332.589492169706197.88
5115.4616666666671.574494627915045.06






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-8.44943176785705
beta0.0896593804564008
S.D.0.0917561712933909
T-STAT0.977148230931677
p-value0.400559718098802

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -8.44943176785705 \tabularnewline
beta & 0.0896593804564008 \tabularnewline
S.D. & 0.0917561712933909 \tabularnewline
T-STAT & 0.977148230931677 \tabularnewline
p-value & 0.400559718098802 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164709&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-8.44943176785705[/C][/ROW]
[ROW][C]beta[/C][C]0.0896593804564008[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0917561712933909[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.977148230931677[/C][/ROW]
[ROW][C]p-value[/C][C]0.400559718098802[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164709&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164709&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-8.44943176785705
beta0.0896593804564008
S.D.0.0917561712933909
T-STAT0.977148230931677
p-value0.400559718098802






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-62.3475159221731
beta13.2873121844531
S.D.9.09618476570666
T-STAT1.46075662782789
p-value0.240217766144431
Lambda-12.2873121844531

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -62.3475159221731 \tabularnewline
beta & 13.2873121844531 \tabularnewline
S.D. & 9.09618476570666 \tabularnewline
T-STAT & 1.46075662782789 \tabularnewline
p-value & 0.240217766144431 \tabularnewline
Lambda & -12.2873121844531 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164709&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-62.3475159221731[/C][/ROW]
[ROW][C]beta[/C][C]13.2873121844531[/C][/ROW]
[ROW][C]S.D.[/C][C]9.09618476570666[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.46075662782789[/C][/ROW]
[ROW][C]p-value[/C][C]0.240217766144431[/C][/ROW]
[ROW][C]Lambda[/C][C]-12.2873121844531[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164709&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164709&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-62.3475159221731
beta13.2873121844531
S.D.9.09618476570666
T-STAT1.46075662782789
p-value0.240217766144431
Lambda-12.2873121844531


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')