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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 computationThu, 03 Dec 2009 13:43:41 -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/Dec/03/t1259873178578iu33kgjfnl27.htm/, Retrieved Fri, 29 Mar 2024 06:57:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63117, Retrieved Fri, 29 Mar 2024 06:57:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134
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] [] [2009-11-27 14:40:44] [b98453cac15ba1066b407e146608df68]
F    D      [Standard Deviation-Mean Plot] [workshop 9] [2009-12-03 20:43:41] [6c94b261890ba36343a04d1029691995] [Current]
Feedback Forum
2009-12-10 18:43:32 [a29ecf012646440cb204d2a87bf5881a] [reply
Je bewerking is juist en ook je conclusie is correct. Toch
wil ik nog snel 2 dingen toevoegen:
1)de 'alpha' die je hier ziet staan is niet de alfafout maar
gewoon een waarde (zoals 'a' in de vergelijking y=ax+b).
De echte alfafout kies je zelf, afhankelijk van je doelstellingen.

2)Je stelt dat aangezien p-waarde = 96.25%, dat het resultaat dan
significant verschillend van 0 is. Het omgekeerde is echter waar.
Het resultaat is significant verschillend van 0 wanneer de p-waarde
zeer dicht tegen 0 aanleunt.

De conclusie dat Ho niet verworpen wordt is wel correct.

Post a new message
Dataseries X:
283.042
276.687
277.915
277.128
277.103
275.037
270.150
267.140
264.993
287.259
291.186
292.300
288.186
281.477
282.656
280.190
280.408
276.836
275.216
274.352
271.311
289.802
290.726
292.300
278.506
269.826
265.861
269.034
264.176
255.198
253.353
246.057
235.372
258.556
260.993
254.663
250.643
243.422
247.105
248.541
245.039
237.080
237.085
225.554
226.839
247.934
248.333
246.969
245.098
246.263
255.765
264.319
268.347
273.046
273.963
267.430
271.993
292.710
295.881
293.299




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63117&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63117&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63117&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
1278.3283333333338.7855124167143827.307
2281.9556.9628702415024320.9890000000000
3259.29958333333311.545486969204043.1340000000000
4242.0453333333338.5465610663357525.089
5270.67616666666717.001828371375150.783

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 278.328333333333 & 8.78551241671438 & 27.307 \tabularnewline
2 & 281.955 & 6.96287024150243 & 20.9890000000000 \tabularnewline
3 & 259.299583333333 & 11.5454869692040 & 43.1340000000000 \tabularnewline
4 & 242.045333333333 & 8.54656106633575 & 25.089 \tabularnewline
5 & 270.676166666667 & 17.0018283713751 & 50.783 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63117&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]278.328333333333[/C][C]8.78551241671438[/C][C]27.307[/C][/ROW]
[ROW][C]2[/C][C]281.955[/C][C]6.96287024150243[/C][C]20.9890000000000[/C][/ROW]
[ROW][C]3[/C][C]259.299583333333[/C][C]11.5454869692040[/C][C]43.1340000000000[/C][/ROW]
[ROW][C]4[/C][C]242.045333333333[/C][C]8.54656106633575[/C][C]25.089[/C][/ROW]
[ROW][C]5[/C][C]270.676166666667[/C][C]17.0018283713751[/C][C]50.783[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63117&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63117&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
1278.3283333333338.7855124167143827.307
2281.9556.9628702415024320.9890000000000
3259.29958333333311.545486969204043.1340000000000
4242.0453333333338.5465610663357525.089
5270.67616666666717.001828371375150.783







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha12.4883103269781
beta-0.00720502945848929
S.D.0.141185931171156
T-STAT-0.0510322055372135
p-value0.962507654213884

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 12.4883103269781 \tabularnewline
beta & -0.00720502945848929 \tabularnewline
S.D. & 0.141185931171156 \tabularnewline
T-STAT & -0.0510322055372135 \tabularnewline
p-value & 0.962507654213884 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63117&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]12.4883103269781[/C][/ROW]
[ROW][C]beta[/C][C]-0.00720502945848929[/C][/ROW]
[ROW][C]S.D.[/C][C]0.141185931171156[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.0510322055372135[/C][/ROW]
[ROW][C]p-value[/C][C]0.962507654213884[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63117&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63117&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)
alpha12.4883103269781
beta-0.00720502945848929
S.D.0.141185931171156
T-STAT-0.0510322055372135
p-value0.962507654213884







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha4.69874240935039
beta-0.428204843987789
S.D.3.20565134900264
T-STAT-0.133578108586579
p-value0.902193318539357
Lambda1.42820484398779

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 4.69874240935039 \tabularnewline
beta & -0.428204843987789 \tabularnewline
S.D. & 3.20565134900264 \tabularnewline
T-STAT & -0.133578108586579 \tabularnewline
p-value & 0.902193318539357 \tabularnewline
Lambda & 1.42820484398779 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63117&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]4.69874240935039[/C][/ROW]
[ROW][C]beta[/C][C]-0.428204843987789[/C][/ROW]
[ROW][C]S.D.[/C][C]3.20565134900264[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.133578108586579[/C][/ROW]
[ROW][C]p-value[/C][C]0.902193318539357[/C][/ROW]
[ROW][C]Lambda[/C][C]1.42820484398779[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63117&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63117&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)
alpha4.69874240935039
beta-0.428204843987789
S.D.3.20565134900264
T-STAT-0.133578108586579
p-value0.902193318539357
Lambda1.42820484398779



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')