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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 computationTue, 04 Oct 2011 05:53:55 -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/2011/Oct/04/t131772206088cr8rvtpuhqjv6.htm/, Retrieved Thu, 31 Oct 2024 23:29:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=125706, Retrieved Thu, 31 Oct 2024 23:29:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact177
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Variability] [Variability of th...] [2010-09-25 09:46:38] [b98453cac15ba1066b407e146608df68]
- RMP     [Standard Deviation-Mean Plot] [Taak 5: Variability ] [2011-10-04 09:53:55] [a30255a61f2fa55603799e7bfe8f38bd] [Current]
- RM        [Standard Deviation-Mean Plot] [Workshop 1 - Task 5] [2013-10-08 07:11:48] [dcd8ad28d6468682e840b291a8f08031]
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Dataseries X:
426.113
383.703
232.444
70.939
226.731
947.293
611.281
158.047
33.999
37.028
388.3
506.652
392.25
180.818
198.296
217.465
275.562
1030.944
57.47
136.452
556.277
213.361
274.482
220.553
236.71
260.642
2763.544
213.923
169.861
403.064
449.594
406.167
206.893
156.187
257.102
62.156
662.883
251.422
171.328
350.089
221.588
4.813
183.186
190.379
223.166
232.669
356.725
109.215
475.834
315.955
694.87
8.95
278.741
308.16
207.533
192.797
601.162
289.714
293.671
386.688
699.645
85.094
131.812
645.285
197.549
308.174
86.58
242.205
238.502
187.881
140.321
440.31
421.403
218.761
1305.923
137.55
262.517
348.821
150.034
64.016
261.596
259.7
171.26
203.077
249.148
211.655
252.64
438.555
239.89
401.915
216.886
184.641
380.155
653.641
313.906
366.936
236.302
229.641
235.577
103.898
263.906
241.171
216.548
295.281
193.299
204.386
257.567
136.813
240.755
59.609
213.511
380.531
242.344
250.407
183.613
191.835
266.793
246.542
330.563
403.556
208.108
324.04
308.532
199.297
200.156
262.875
287.069
190.157
199.746
265.777
435.956
72.844
756.46
206.771
4202.361
401.422
216.046
39.047
441.437




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=125706&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=125706&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=125706&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' @ jenkins.wessa.net







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
1278.29975161.306987278657355.174
2485.838366.633350185168789.246
3241.49475242.707839990629472.653
4247.2072597.8465527492751211.432
5375.107446.423136492723973.474
6316.16825162.379762645832342.916
7868.704751263.370174762562549.621
8357.1715126.667310296172279.733
9170.584583.2017880356746194.946
10358.9305215.420573109905491.555
11149.991598.2104554193018216.775
12230.44375101.162050379165247.51
13373.90225288.651119728038685.92
14246.8077555.5082520554377115.363
15392.80875145.951317589976311.448
16390.459326.944606269421614.551
17208.62793.193847411368221.594
18251.7535131.941974944797299.989
19520.90925536.7823770755861168.373
20206.347125.010902332557284.805
21223.9082544.374148152387190.336
22287.9995102.070522275859226.9
23260.83396.7458864689692217.274
24428.6595152.69524995559339.735
25201.354565.0394822063235132.404
26254.226533.512585362318278.733
27198.0162549.5141589135539120.754
28223.6015131.550331025809320.922
29217.0497534.187254344029166.794
30311.863570.8412172608574157.014
31259.9942565.4067029305866124.743
32235.0642547.304611680575196.912
33243.58075151.187999341603363.112
341391.75351887.507382360573995.59

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 278.29975 & 161.306987278657 & 355.174 \tabularnewline
2 & 485.838 & 366.633350185168 & 789.246 \tabularnewline
3 & 241.49475 & 242.707839990629 & 472.653 \tabularnewline
4 & 247.20725 & 97.8465527492751 & 211.432 \tabularnewline
5 & 375.107 & 446.423136492723 & 973.474 \tabularnewline
6 & 316.16825 & 162.379762645832 & 342.916 \tabularnewline
7 & 868.70475 & 1263.37017476256 & 2549.621 \tabularnewline
8 & 357.1715 & 126.667310296172 & 279.733 \tabularnewline
9 & 170.5845 & 83.2017880356746 & 194.946 \tabularnewline
10 & 358.9305 & 215.420573109905 & 491.555 \tabularnewline
11 & 149.9915 & 98.2104554193018 & 216.775 \tabularnewline
12 & 230.44375 & 101.162050379165 & 247.51 \tabularnewline
13 & 373.90225 & 288.651119728038 & 685.92 \tabularnewline
14 & 246.80775 & 55.5082520554377 & 115.363 \tabularnewline
15 & 392.80875 & 145.951317589976 & 311.448 \tabularnewline
16 & 390.459 & 326.944606269421 & 614.551 \tabularnewline
17 & 208.627 & 93.193847411368 & 221.594 \tabularnewline
18 & 251.7535 & 131.941974944797 & 299.989 \tabularnewline
19 & 520.90925 & 536.782377075586 & 1168.373 \tabularnewline
20 & 206.347 & 125.010902332557 & 284.805 \tabularnewline
21 & 223.90825 & 44.3741481523871 & 90.336 \tabularnewline
22 & 287.9995 & 102.070522275859 & 226.9 \tabularnewline
23 & 260.833 & 96.7458864689692 & 217.274 \tabularnewline
24 & 428.6595 & 152.69524995559 & 339.735 \tabularnewline
25 & 201.3545 & 65.0394822063235 & 132.404 \tabularnewline
26 & 254.2265 & 33.5125853623182 & 78.733 \tabularnewline
27 & 198.01625 & 49.5141589135539 & 120.754 \tabularnewline
28 & 223.6015 & 131.550331025809 & 320.922 \tabularnewline
29 & 217.04975 & 34.1872543440291 & 66.794 \tabularnewline
30 & 311.8635 & 70.8412172608574 & 157.014 \tabularnewline
31 & 259.99425 & 65.4067029305866 & 124.743 \tabularnewline
32 & 235.06425 & 47.3046116805751 & 96.912 \tabularnewline
33 & 243.58075 & 151.187999341603 & 363.112 \tabularnewline
34 & 1391.7535 & 1887.50738236057 & 3995.59 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=125706&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]278.29975[/C][C]161.306987278657[/C][C]355.174[/C][/ROW]
[ROW][C]2[/C][C]485.838[/C][C]366.633350185168[/C][C]789.246[/C][/ROW]
[ROW][C]3[/C][C]241.49475[/C][C]242.707839990629[/C][C]472.653[/C][/ROW]
[ROW][C]4[/C][C]247.20725[/C][C]97.8465527492751[/C][C]211.432[/C][/ROW]
[ROW][C]5[/C][C]375.107[/C][C]446.423136492723[/C][C]973.474[/C][/ROW]
[ROW][C]6[/C][C]316.16825[/C][C]162.379762645832[/C][C]342.916[/C][/ROW]
[ROW][C]7[/C][C]868.70475[/C][C]1263.37017476256[/C][C]2549.621[/C][/ROW]
[ROW][C]8[/C][C]357.1715[/C][C]126.667310296172[/C][C]279.733[/C][/ROW]
[ROW][C]9[/C][C]170.5845[/C][C]83.2017880356746[/C][C]194.946[/C][/ROW]
[ROW][C]10[/C][C]358.9305[/C][C]215.420573109905[/C][C]491.555[/C][/ROW]
[ROW][C]11[/C][C]149.9915[/C][C]98.2104554193018[/C][C]216.775[/C][/ROW]
[ROW][C]12[/C][C]230.44375[/C][C]101.162050379165[/C][C]247.51[/C][/ROW]
[ROW][C]13[/C][C]373.90225[/C][C]288.651119728038[/C][C]685.92[/C][/ROW]
[ROW][C]14[/C][C]246.80775[/C][C]55.5082520554377[/C][C]115.363[/C][/ROW]
[ROW][C]15[/C][C]392.80875[/C][C]145.951317589976[/C][C]311.448[/C][/ROW]
[ROW][C]16[/C][C]390.459[/C][C]326.944606269421[/C][C]614.551[/C][/ROW]
[ROW][C]17[/C][C]208.627[/C][C]93.193847411368[/C][C]221.594[/C][/ROW]
[ROW][C]18[/C][C]251.7535[/C][C]131.941974944797[/C][C]299.989[/C][/ROW]
[ROW][C]19[/C][C]520.90925[/C][C]536.782377075586[/C][C]1168.373[/C][/ROW]
[ROW][C]20[/C][C]206.347[/C][C]125.010902332557[/C][C]284.805[/C][/ROW]
[ROW][C]21[/C][C]223.90825[/C][C]44.3741481523871[/C][C]90.336[/C][/ROW]
[ROW][C]22[/C][C]287.9995[/C][C]102.070522275859[/C][C]226.9[/C][/ROW]
[ROW][C]23[/C][C]260.833[/C][C]96.7458864689692[/C][C]217.274[/C][/ROW]
[ROW][C]24[/C][C]428.6595[/C][C]152.69524995559[/C][C]339.735[/C][/ROW]
[ROW][C]25[/C][C]201.3545[/C][C]65.0394822063235[/C][C]132.404[/C][/ROW]
[ROW][C]26[/C][C]254.2265[/C][C]33.5125853623182[/C][C]78.733[/C][/ROW]
[ROW][C]27[/C][C]198.01625[/C][C]49.5141589135539[/C][C]120.754[/C][/ROW]
[ROW][C]28[/C][C]223.6015[/C][C]131.550331025809[/C][C]320.922[/C][/ROW]
[ROW][C]29[/C][C]217.04975[/C][C]34.1872543440291[/C][C]66.794[/C][/ROW]
[ROW][C]30[/C][C]311.8635[/C][C]70.8412172608574[/C][C]157.014[/C][/ROW]
[ROW][C]31[/C][C]259.99425[/C][C]65.4067029305866[/C][C]124.743[/C][/ROW]
[ROW][C]32[/C][C]235.06425[/C][C]47.3046116805751[/C][C]96.912[/C][/ROW]
[ROW][C]33[/C][C]243.58075[/C][C]151.187999341603[/C][C]363.112[/C][/ROW]
[ROW][C]34[/C][C]1391.7535[/C][C]1887.50738236057[/C][C]3995.59[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=125706&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=125706&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
1278.29975161.306987278657355.174
2485.838366.633350185168789.246
3241.49475242.707839990629472.653
4247.2072597.8465527492751211.432
5375.107446.423136492723973.474
6316.16825162.379762645832342.916
7868.704751263.370174762562549.621
8357.1715126.667310296172279.733
9170.584583.2017880356746194.946
10358.9305215.420573109905491.555
11149.991598.2104554193018216.775
12230.44375101.162050379165247.51
13373.90225288.651119728038685.92
14246.8077555.5082520554377115.363
15392.80875145.951317589976311.448
16390.459326.944606269421614.551
17208.62793.193847411368221.594
18251.7535131.941974944797299.989
19520.90925536.7823770755861168.373
20206.347125.010902332557284.805
21223.9082544.374148152387190.336
22287.9995102.070522275859226.9
23260.83396.7458864689692217.274
24428.6595152.69524995559339.735
25201.354565.0394822063235132.404
26254.226533.512585362318278.733
27198.0162549.5141589135539120.754
28223.6015131.550331025809320.922
29217.0497534.187254344029166.794
30311.863570.8412172608574157.014
31259.9942565.4067029305866124.743
32235.0642547.304611680575196.912
33243.58075151.187999341603363.112
341391.75351887.507382360573995.59







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-285.716208632923
beta1.5527116490911
S.D.0.0729424697603627
T-STAT21.2867984069118
p-value1.85606828640576e-20

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -285.716208632923 \tabularnewline
beta & 1.5527116490911 \tabularnewline
S.D. & 0.0729424697603627 \tabularnewline
T-STAT & 21.2867984069118 \tabularnewline
p-value & 1.85606828640576e-20 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=125706&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-285.716208632923[/C][/ROW]
[ROW][C]beta[/C][C]1.5527116490911[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0729424697603627[/C][/ROW]
[ROW][C]T-STAT[/C][C]21.2867984069118[/C][/ROW]
[ROW][C]p-value[/C][C]1.85606828640576e-20[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=125706&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=125706&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)
alpha-285.716208632923
beta1.5527116490911
S.D.0.0729424697603627
T-STAT21.2867984069118
p-value1.85606828640576e-20







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-5.02351295173873
beta1.74572412405503
S.D.0.203972215699032
T-STAT8.55863686175232
p-value8.83256044869692e-10
Lambda-0.745724124055028

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -5.02351295173873 \tabularnewline
beta & 1.74572412405503 \tabularnewline
S.D. & 0.203972215699032 \tabularnewline
T-STAT & 8.55863686175232 \tabularnewline
p-value & 8.83256044869692e-10 \tabularnewline
Lambda & -0.745724124055028 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=125706&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-5.02351295173873[/C][/ROW]
[ROW][C]beta[/C][C]1.74572412405503[/C][/ROW]
[ROW][C]S.D.[/C][C]0.203972215699032[/C][/ROW]
[ROW][C]T-STAT[/C][C]8.55863686175232[/C][/ROW]
[ROW][C]p-value[/C][C]8.83256044869692e-10[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.745724124055028[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=125706&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=125706&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)
alpha-5.02351295173873
beta1.74572412405503
S.D.0.203972215699032
T-STAT8.55863686175232
p-value8.83256044869692e-10
Lambda-0.745724124055028



Parameters (Session):
par1 = 4 ;
Parameters (R input):
par1 = 4 ;
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')