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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 13:36:31 +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/t1291383315v0ezatt4v31nuvq.htm/, Retrieved Tue, 07 May 2024 18:01:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104771, Retrieved Tue, 07 May 2024 18:01:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact140

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] [Workshop 9] [2010-12-03 13:36:31] [ecfb965f5669057f3ac5b58964283289] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
63.152
60.106
72.616
73.159
68.848
77.056
62.246
60.777
64.513
58.353
56.511
44.554
71.414
65.719
80.997
69.826
65.386
75.589
65.520
59.003
63.961
59.716
57.520
42.886
69.805
64.656
80.353
71.321
76.577
81.580
71.127
63.478
48.152
69.236
57.038
43.621
69.551
72.009
72.140
81.519
73.310
80.406
70.697
59.328
68.281
70.041
51.244
46.538
61.443
62.256
73.117
74.155
65.191
77.889
68.688
59.983
65.470
65.089
54.795
47.123

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' @ 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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104771&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' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104771&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104771&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' @ 72.249.76.132






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
163.49091666666678.7630701703573132.502
264.794759.7366925981053738.111
366.41211.855128962911937.959
467.92210.568028912285834.981
564.59991666666678.5112391412588730.766

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 63.4909166666667 & 8.76307017035731 & 32.502 \tabularnewline
2 & 64.79475 & 9.73669259810537 & 38.111 \tabularnewline
3 & 66.412 & 11.8551289629119 & 37.959 \tabularnewline
4 & 67.922 & 10.5680289122858 & 34.981 \tabularnewline
5 & 64.5999166666667 & 8.51123914125887 & 30.766 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104771&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]63.4909166666667[/C][C]8.76307017035731[/C][C]32.502[/C][/ROW]
[ROW][C]2[/C][C]64.79475[/C][C]9.73669259810537[/C][C]38.111[/C][/ROW]
[ROW][C]3[/C][C]66.412[/C][C]11.8551289629119[/C][C]37.959[/C][/ROW]
[ROW][C]4[/C][C]67.922[/C][C]10.5680289122858[/C][C]34.981[/C][/ROW]
[ROW][C]5[/C][C]64.5999166666667[/C][C]8.51123914125887[/C][C]30.766[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104771&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104771&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
163.49091666666678.7630701703573132.502
264.794759.7366925981053738.111
366.41211.855128962911937.959
467.92210.568028912285834.981
564.59991666666678.5112391412588730.766






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-28.4603032378957
beta0.585954159653947
S.D.0.306389186299899
T-STAT1.91245052323878
p-value0.151759687009359

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -28.4603032378957 \tabularnewline
beta & 0.585954159653947 \tabularnewline
S.D. & 0.306389186299899 \tabularnewline
T-STAT & 1.91245052323878 \tabularnewline
p-value & 0.151759687009359 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104771&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-28.4603032378957[/C][/ROW]
[ROW][C]beta[/C][C]0.585954159653947[/C][/ROW]
[ROW][C]S.D.[/C][C]0.306389186299899[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.91245052323878[/C][/ROW]
[ROW][C]p-value[/C][C]0.151759687009359[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104771&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104771&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-28.4603032378957
beta0.585954159653947
S.D.0.306389186299899
T-STAT1.91245052323878
p-value0.151759687009359






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-14.0722554727588
beta3.91204878178576
S.D.1.95111074212804
T-STAT2.00503677075703
p-value0.138647860526576
Lambda-2.91204878178576

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -14.0722554727588 \tabularnewline
beta & 3.91204878178576 \tabularnewline
S.D. & 1.95111074212804 \tabularnewline
T-STAT & 2.00503677075703 \tabularnewline
p-value & 0.138647860526576 \tabularnewline
Lambda & -2.91204878178576 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104771&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-14.0722554727588[/C][/ROW]
[ROW][C]beta[/C][C]3.91204878178576[/C][/ROW]
[ROW][C]S.D.[/C][C]1.95111074212804[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.00503677075703[/C][/ROW]
[ROW][C]p-value[/C][C]0.138647860526576[/C][/ROW]
[ROW][C]Lambda[/C][C]-2.91204878178576[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104771&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104771&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-14.0722554727588
beta3.91204878178576
S.D.1.95111074212804
T-STAT2.00503677075703
p-value0.138647860526576
Lambda-2.91204878178576


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')