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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationSun, 28 Apr 2013 08:44:03 -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/2013/Apr/28/t13671531755l15clsmp4ovdkt.htm/, Retrieved Fri, 03 May 2024 19:37:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=208441, Retrieved Fri, 03 May 2024 19:37:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact158

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] [] [2013-04-28 12:44:03] [4d26c4233714d8e4ecf99606d744931b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
102.42
102.46
102.76
102.4
102.47
102.27
102.17
101.84
102.13
103.34
103.43
103.59
104.21
105.42
105.95
106.28
106.49
106.49
106.49
107.38
108.69
108.76
108.84
108.67
108.79
109.96
110.86
111
111.84
112.21
112.4
113.76
114.85
115.23
115.39
115.29
115.53
116.26
116.85
117.37
118.03
118.49
119.32
119.4
122.26
122.91
123.78
123.99
124.7
125.89
127.57
128.97
130.65
130.73
130.95
131.36
132.85
133.08
133.13
133.27
133.9
134.85
135.49
136.21
136.31
136.22
136.22
135.51
137.3
138.42
138.92
138.67

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'George Udny Yule' @ yule.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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=208441&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=208441&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=208441&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'George Udny Yule' @ yule.wessa.net






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
1102.6066666666670.5594531962442271.75
2106.97251.505904288881844.63000000000001
3112.6316666666672.259850491166236.59999999999999
4119.5158333333332.996399227962348.45999999999999
5130.26252.906532219923698.57000000000001
6136.5016666666671.556551160467115.01999999999998

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 102.606666666667 & 0.559453196244227 & 1.75 \tabularnewline
2 & 106.9725 & 1.50590428888184 & 4.63000000000001 \tabularnewline
3 & 112.631666666667 & 2.25985049116623 & 6.59999999999999 \tabularnewline
4 & 119.515833333333 & 2.99639922796234 & 8.45999999999999 \tabularnewline
5 & 130.2625 & 2.90653221992369 & 8.57000000000001 \tabularnewline
6 & 136.501666666667 & 1.55655116046711 & 5.01999999999998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=208441&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]102.606666666667[/C][C]0.559453196244227[/C][C]1.75[/C][/ROW]
[ROW][C]2[/C][C]106.9725[/C][C]1.50590428888184[/C][C]4.63000000000001[/C][/ROW]
[ROW][C]3[/C][C]112.631666666667[/C][C]2.25985049116623[/C][C]6.59999999999999[/C][/ROW]
[ROW][C]4[/C][C]119.515833333333[/C][C]2.99639922796234[/C][C]8.45999999999999[/C][/ROW]
[ROW][C]5[/C][C]130.2625[/C][C]2.90653221992369[/C][C]8.57000000000001[/C][/ROW]
[ROW][C]6[/C][C]136.501666666667[/C][C]1.55655116046711[/C][C]5.01999999999998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=208441&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=208441&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
1102.6066666666670.5594531962442271.75
2106.97251.505904288881844.63000000000001
3112.6316666666672.259850491166236.59999999999999
4119.5158333333332.996399227962348.45999999999999
5130.26252.906532219923698.57000000000001
6136.5016666666671.556551160467115.01999999999998






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-2.14026521644787
beta0.0347587868815042
S.D.0.030691072578704
T-STAT1.13253737849562
p-value0.320695009419984

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -2.14026521644787 \tabularnewline
beta & 0.0347587868815042 \tabularnewline
S.D. & 0.030691072578704 \tabularnewline
T-STAT & 1.13253737849562 \tabularnewline
p-value & 0.320695009419984 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=208441&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-2.14026521644787[/C][/ROW]
[ROW][C]beta[/C][C]0.0347587868815042[/C][/ROW]
[ROW][C]S.D.[/C][C]0.030691072578704[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.13253737849562[/C][/ROW]
[ROW][C]p-value[/C][C]0.320695009419984[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=208441&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=208441&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-2.14026521644787
beta0.0347587868815042
S.D.0.030691072578704
T-STAT1.13253737849562
p-value0.320695009419984






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-14.8750738964862
beta3.23465381833939
S.D.2.27796713309229
T-STAT1.41997387554421
p-value0.228637194216122
Lambda-2.23465381833939

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -14.8750738964862 \tabularnewline
beta & 3.23465381833939 \tabularnewline
S.D. & 2.27796713309229 \tabularnewline
T-STAT & 1.41997387554421 \tabularnewline
p-value & 0.228637194216122 \tabularnewline
Lambda & -2.23465381833939 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=208441&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-14.8750738964862[/C][/ROW]
[ROW][C]beta[/C][C]3.23465381833939[/C][/ROW]
[ROW][C]S.D.[/C][C]2.27796713309229[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.41997387554421[/C][/ROW]
[ROW][C]p-value[/C][C]0.228637194216122[/C][/ROW]
[ROW][C]Lambda[/C][C]-2.23465381833939[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=208441&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=208441&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-14.8750738964862
beta3.23465381833939
S.D.2.27796713309229
T-STAT1.41997387554421
p-value0.228637194216122
Lambda-2.23465381833939


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')