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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 computationFri, 03 Dec 2010 13:36:32 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/03/t1291383329anos1x5ut3p6tg9.htm/, Retrieved Wed, 08 May 2024 02:51:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104773, Retrieved Wed, 08 May 2024 02:51:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
-   PD      [Standard Deviation-Mean Plot] [SMP] [2010-12-03 13:36:32] [7b4029fa8534fd52dfa7d68267386cff] [Current]
-    D        [Standard Deviation-Mean Plot] [] [2010-12-07 16:06:54] [ed939ef6f97e5f2afb6796311d9e7a5f]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
62.027
56.493
65.566
62.653
53.470
59.600
42.542
42.018
44.038
44.988
43.309
26.843
69.770
64.886
79.354
63.025
54.003
55.926
45.629
40.361
43.039
44.570
43.269
25.563
68.707
60.223
74.283
61.232
61.531
65.305
51.699
44.599
35.221
55.066
45.335
28.702
69.517
69.240
71.525
77.740
62.107
65.450
51.493
43.067
49.172
54.483
38.158
27.898
58.648
56.000
62.381
59.849
48.345
55.376
45.400
38.389
44.098
48.290
41.267
31.238

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104773&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]3 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=104773&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104773&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 time3 seconds
R Server'George Udny Yule' @ 72.249.76.132






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
150.295583333333311.494563200672038.723
252.449583333333314.940243191755253.791
354.3252513.699716832674145.581
456.654166666666715.188753715306749.842
549.106759.5591768295753931.143

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 50.2955833333333 & 11.4945632006720 & 38.723 \tabularnewline
2 & 52.4495833333333 & 14.9402431917552 & 53.791 \tabularnewline
3 & 54.32525 & 13.6997168326741 & 45.581 \tabularnewline
4 & 56.6541666666667 & 15.1887537153067 & 49.842 \tabularnewline
5 & 49.10675 & 9.55917682957539 & 31.143 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104773&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]50.2955833333333[/C][C]11.4945632006720[/C][C]38.723[/C][/ROW]
[ROW][C]2[/C][C]52.4495833333333[/C][C]14.9402431917552[/C][C]53.791[/C][/ROW]
[ROW][C]3[/C][C]54.32525[/C][C]13.6997168326741[/C][C]45.581[/C][/ROW]
[ROW][C]4[/C][C]56.6541666666667[/C][C]15.1887537153067[/C][C]49.842[/C][/ROW]
[ROW][C]5[/C][C]49.10675[/C][C]9.55917682957539[/C][C]31.143[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104773&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104773&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
150.295583333333311.494563200672038.723
252.449583333333314.940243191755253.791
354.3252513.699716832674145.581
456.654166666666715.188753715306749.842
549.106759.5591768295753931.143






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-22.9857616892914
beta0.684131758325772
S.D.0.229886684066430
T-STAT2.97595209180572
p-value0.0587871544887271

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -22.9857616892914 \tabularnewline
beta & 0.684131758325772 \tabularnewline
S.D. & 0.229886684066430 \tabularnewline
T-STAT & 2.97595209180572 \tabularnewline
p-value & 0.0587871544887271 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104773&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-22.9857616892914[/C][/ROW]
[ROW][C]beta[/C][C]0.684131758325772[/C][/ROW]
[ROW][C]S.D.[/C][C]0.229886684066430[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.97595209180572[/C][/ROW]
[ROW][C]p-value[/C][C]0.0587871544887271[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104773&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104773&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-22.9857616892914
beta0.684131758325772
S.D.0.229886684066430
T-STAT2.97595209180572
p-value0.0587871544887271






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-9.18663115700836
beta2.96280284793311
S.D.0.9779926029761
T-STAT3.02947367793692
p-value0.0563344739372153
Lambda-1.96280284793311

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -9.18663115700836 \tabularnewline
beta & 2.96280284793311 \tabularnewline
S.D. & 0.9779926029761 \tabularnewline
T-STAT & 3.02947367793692 \tabularnewline
p-value & 0.0563344739372153 \tabularnewline
Lambda & -1.96280284793311 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104773&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-9.18663115700836[/C][/ROW]
[ROW][C]beta[/C][C]2.96280284793311[/C][/ROW]
[ROW][C]S.D.[/C][C]0.9779926029761[/C][/ROW]
[ROW][C]T-STAT[/C][C]3.02947367793692[/C][/ROW]
[ROW][C]p-value[/C][C]0.0563344739372153[/C][/ROW]
[ROW][C]Lambda[/C][C]-1.96280284793311[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104773&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104773&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-9.18663115700836
beta2.96280284793311
S.D.0.9779926029761
T-STAT3.02947367793692
p-value0.0563344739372153
Lambda-1.96280284793311


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')