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Author's title

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationThu, 08 Jan 2009 04:29:09 -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/2009/Jan/08/t1231414272yxysjmkoof2944e.htm/, Retrieved Wed, 08 May 2024 19:00:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=36821, Retrieved Wed, 08 May 2024 19:00:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact237
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Standard Deviation-Mean Plot] [2MAR03A_Robbe Ley...] [2009-01-08 11:29:09] [5cfda051308d8cc79b9da3748118f98f] [Current]
-   P     [Standard Deviation-Mean Plot] [Robbe Leys_2MAR03...] [2009-01-27 16:39:47] [74be16979710d4c4e7c6647856088456]
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Dataseries X:
284.4
212.8
226.9
308.4
262
227.9
236.1
320.4
271.9
232.8
237
313.4
261.4
226.8
249.9
314.3
286.1
226.5
260.4
311.4
294.7
232.6
257.2
339.2
279.1
249.8
269.8
345.7
293.8
254.7
277.5
363.4
313.4
272.8
300.1
369.5
330.8
287.8
305.9
386.1
335.2
288
308.3
402.3
352.8
316.1
324.9
404.8
393
318.9
327
442.3
383.1
331.6
361.4
445.9
386.6
357.2
373.6
466.2
409.6
369.8
378.6
487
419.2
376.7
392.8
506.1
458.4
387.4
426.9
565
464.8
444.5
449.5
556.1
499.6
451.9
434.9
553.8
510
432.9
453.2
547.6
485.8
452.6
456.6
565.7
514.8
464.3
430.9
588.3
503.1
442.6
448
554.5
504.5
427.3
473.1
526.2
547.5
440.2
468.7
574.5
492.6
432.6
479.8
575.7
474.6
405.3
434.6
535.1
452.6
429.5
417.2
551.8
464
416.6
422.9
553.6
458.6
427.6
429.2
534.2
481.7
416
440.2
538.7
473.8
439.9
446.8
597.5
467.2
439.4
447.4
568.5
485.9
442.1
430.5
600
464.5
423.6
437
574
443
410
420
532
432
420
411
512




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36821&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36821&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36821&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
1261.16666666666737.8523767317512107.6
2271.70833333333337.1613058370552112.7
3299.13333333333340.8037060111263119.7
4336.91666666666741.2957588254496117
5382.23333333333348.2497165951796147.3
6431.45833333333360.9725341792714195.2
7483.23333333333347.8139639803599123.2
8492.26666666666753.0648374132718157.4
9495.22551.821601761147148.4
10463.1554.4829998506623148.3
11473.68333333333355.5360298010274181.5
12481.67562.5962694938401176.4

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 261.166666666667 & 37.8523767317512 & 107.6 \tabularnewline
2 & 271.708333333333 & 37.1613058370552 & 112.7 \tabularnewline
3 & 299.133333333333 & 40.8037060111263 & 119.7 \tabularnewline
4 & 336.916666666667 & 41.2957588254496 & 117 \tabularnewline
5 & 382.233333333333 & 48.2497165951796 & 147.3 \tabularnewline
6 & 431.458333333333 & 60.9725341792714 & 195.2 \tabularnewline
7 & 483.233333333333 & 47.8139639803599 & 123.2 \tabularnewline
8 & 492.266666666667 & 53.0648374132718 & 157.4 \tabularnewline
9 & 495.225 & 51.821601761147 & 148.4 \tabularnewline
10 & 463.15 & 54.4829998506623 & 148.3 \tabularnewline
11 & 473.683333333333 & 55.5360298010274 & 181.5 \tabularnewline
12 & 481.675 & 62.5962694938401 & 176.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36821&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]261.166666666667[/C][C]37.8523767317512[/C][C]107.6[/C][/ROW]
[ROW][C]2[/C][C]271.708333333333[/C][C]37.1613058370552[/C][C]112.7[/C][/ROW]
[ROW][C]3[/C][C]299.133333333333[/C][C]40.8037060111263[/C][C]119.7[/C][/ROW]
[ROW][C]4[/C][C]336.916666666667[/C][C]41.2957588254496[/C][C]117[/C][/ROW]
[ROW][C]5[/C][C]382.233333333333[/C][C]48.2497165951796[/C][C]147.3[/C][/ROW]
[ROW][C]6[/C][C]431.458333333333[/C][C]60.9725341792714[/C][C]195.2[/C][/ROW]
[ROW][C]7[/C][C]483.233333333333[/C][C]47.8139639803599[/C][C]123.2[/C][/ROW]
[ROW][C]8[/C][C]492.266666666667[/C][C]53.0648374132718[/C][C]157.4[/C][/ROW]
[ROW][C]9[/C][C]495.225[/C][C]51.821601761147[/C][C]148.4[/C][/ROW]
[ROW][C]10[/C][C]463.15[/C][C]54.4829998506623[/C][C]148.3[/C][/ROW]
[ROW][C]11[/C][C]473.683333333333[/C][C]55.5360298010274[/C][C]181.5[/C][/ROW]
[ROW][C]12[/C][C]481.675[/C][C]62.5962694938401[/C][C]176.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36821&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36821&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
1261.16666666666737.8523767317512107.6
2271.70833333333337.1613058370552112.7
3299.13333333333340.8037060111263119.7
4336.91666666666741.2957588254496117
5382.23333333333348.2497165951796147.3
6431.45833333333360.9725341792714195.2
7483.23333333333347.8139639803599123.2
8492.26666666666753.0648374132718157.4
9495.22551.821601761147148.4
10463.1554.4829998506623148.3
11473.68333333333355.5360298010274181.5
12481.67562.5962694938401176.4







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha17.1720954283701
beta0.079145695236861
S.D.0.0163763213206841
T-STAT4.83293492396831
p-value0.000688743839909684

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 17.1720954283701 \tabularnewline
beta & 0.079145695236861 \tabularnewline
S.D. & 0.0163763213206841 \tabularnewline
T-STAT & 4.83293492396831 \tabularnewline
p-value & 0.000688743839909684 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36821&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]17.1720954283701[/C][/ROW]
[ROW][C]beta[/C][C]0.079145695236861[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0163763213206841[/C][/ROW]
[ROW][C]T-STAT[/C][C]4.83293492396831[/C][/ROW]
[ROW][C]p-value[/C][C]0.000688743839909684[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36821&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36821&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)
alpha17.1720954283701
beta0.079145695236861
S.D.0.0163763213206841
T-STAT4.83293492396831
p-value0.000688743839909684







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha0.0647864102481687
beta0.638565360052714
S.D.0.110565013804647
T-STAT5.7754739775185
p-value0.000178835422947581
Lambda0.361434639947286

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 0.0647864102481687 \tabularnewline
beta & 0.638565360052714 \tabularnewline
S.D. & 0.110565013804647 \tabularnewline
T-STAT & 5.7754739775185 \tabularnewline
p-value & 0.000178835422947581 \tabularnewline
Lambda & 0.361434639947286 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36821&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.0647864102481687[/C][/ROW]
[ROW][C]beta[/C][C]0.638565360052714[/C][/ROW]
[ROW][C]S.D.[/C][C]0.110565013804647[/C][/ROW]
[ROW][C]T-STAT[/C][C]5.7754739775185[/C][/ROW]
[ROW][C]p-value[/C][C]0.000178835422947581[/C][/ROW]
[ROW][C]Lambda[/C][C]0.361434639947286[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36821&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36821&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)
alpha0.0647864102481687
beta0.638565360052714
S.D.0.110565013804647
T-STAT5.7754739775185
p-value0.000178835422947581
Lambda0.361434639947286



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')