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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationWed, 03 Dec 2008 06:08:33 -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/2008/Dec/03/t1228309947v3680mdhi7h0np6.htm/, Retrieved Fri, 17 May 2024 16:24:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28683, Retrieved Fri, 17 May 2024 16:24:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact209

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Standard Deviation-Mean Plot] [paper standart de...] [2008-12-03 13:08:33] [b09437381d488816ab9f5cf07e347c02] [Current]
- RMPD    [(Partial) Autocorrelation Function] [paper autocorrela...] [2008-12-03 15:01:26] [f58cc3b532da25682c394745f1a82535]
-    D      [(Partial) Autocorrelation Function] [] [2008-12-03 15:17:29] [f58cc3b532da25682c394745f1a82535]
-    D      [(Partial) Autocorrelation Function] [paper autocorrela...] [2008-12-03 15:17:29] [f58cc3b532da25682c394745f1a82535]
- RM D      [Spectral Analysis] [paper spectral an...] [2008-12-03 15:20:34] [f58cc3b532da25682c394745f1a82535]
- RMPD    [Variance Reduction Matrix] [paper variance re...] [2008-12-03 15:04:03] [f58cc3b532da25682c394745f1a82535]
- RMPD    [Spectral Analysis] [paper spectral an...] [2008-12-03 15:06:49] [f58cc3b532da25682c394745f1a82535]
-   PD    [Standard Deviation-Mean Plot] [paper standard de...] [2008-12-03 15:10:43] [f58cc3b532da25682c394745f1a82535]
-         [Standard Deviation-Mean Plot] [] [2008-12-17 16:55:27] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2008-12-13 14:02:17 [Ken Wright] [reply] 
Hier heb ik een fout gemaakt, de computer berekent altijd een lambda, maar in dit geval moet je hem niet gebruiken. de p value is hoger als 0.005, dit wijst dat de waarde van beta niet significant is van 0. En dit betekent dat er dan geen verband is tussen de SD en het gemiddelde
2008-12-14 18:48:13 [Vincent Vanden Poel] [reply] 
Je hebt je fout zelf al verbeterd (op een typfout na, 0,05 ipv 0,005). Het is hier inderdaad onnodig om de lambda waarde te gebruiken en moet in de modelvergelijking gelijk blijven aan 1.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2659.81
2638.53
2720.25
2745.88
2735.7
2811.7
2799.43
2555.28
2304.98
2214.95
2065.81
1940.49
2042
1995.37
1946.81
1765.9
1635.25
1833.42
1910.43
1959.67
1969.6
2061.41
2093.48
2120.88
2174.56
2196.72
2350.44
2440.25
2408.64
2472.81
2407.6
2454.62
2448.05
2497.84
2645.64
2756.76
2849.27
2921.44
2981.85
3080.58
3106.22
3119.31
3061.26
3097.31
3161.69
3257.16
3277.01
3295.32
3363.99
3494.17
3667.03
3813.06
3917.96
3895.51
3801.06
3570.12
3701.61
3862.27
3970.1
4138.52
4199.75
4290.89
4443.91
4502.64
4356.98
4591.27
4696.96
4621.4
4562.84
4202.52
4296.49
4435.23
4105.18
4116.68
3844.49
3720.98
3674.4
3857.62
3801.06
3504.37
3032.6
3047.03

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28683&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28683&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28683&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 time1 seconds
R Server'George Udny Yule' @ 72.249.76.132






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
12516.0675304.045920817743871.21
21944.51833333333141.731315769300485.63
32437.8275161.517349595808582.2
43100.70166666667137.836137815447446.05
53766.28333333333217.593686252542774.53
64433.40666666667166.292423614292497.21

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 2516.0675 & 304.045920817743 & 871.21 \tabularnewline
2 & 1944.51833333333 & 141.731315769300 & 485.63 \tabularnewline
3 & 2437.8275 & 161.517349595808 & 582.2 \tabularnewline
4 & 3100.70166666667 & 137.836137815447 & 446.05 \tabularnewline
5 & 3766.28333333333 & 217.593686252542 & 774.53 \tabularnewline
6 & 4433.40666666667 & 166.292423614292 & 497.21 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28683&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]2516.0675[/C][C]304.045920817743[/C][C]871.21[/C][/ROW]
[ROW][C]2[/C][C]1944.51833333333[/C][C]141.731315769300[/C][C]485.63[/C][/ROW]
[ROW][C]3[/C][C]2437.8275[/C][C]161.517349595808[/C][C]582.2[/C][/ROW]
[ROW][C]4[/C][C]3100.70166666667[/C][C]137.836137815447[/C][C]446.05[/C][/ROW]
[ROW][C]5[/C][C]3766.28333333333[/C][C]217.593686252542[/C][C]774.53[/C][/ROW]
[ROW][C]6[/C][C]4433.40666666667[/C][C]166.292423614292[/C][C]497.21[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28683&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28683&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
12516.0675304.045920817743871.21
21944.51833333333141.731315769300485.63
32437.8275161.517349595808582.2
43100.70166666667137.836137815447446.05
53766.28333333333217.593686252542774.53
64433.40666666667166.292423614292497.21






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha192.363218566388
beta-0.00138264449414098
S.D.0.034200629166372
T-STAT-0.0404274578521636
p-value0.96968972621515

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 192.363218566388 \tabularnewline
beta & -0.00138264449414098 \tabularnewline
S.D. & 0.034200629166372 \tabularnewline
T-STAT & -0.0404274578521636 \tabularnewline
p-value & 0.96968972621515 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28683&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]192.363218566388[/C][/ROW]
[ROW][C]beta[/C][C]-0.00138264449414098[/C][/ROW]
[ROW][C]S.D.[/C][C]0.034200629166372[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.0404274578521636[/C][/ROW]
[ROW][C]p-value[/C][C]0.96968972621515[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28683&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28683&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)
alpha192.363218566388
beta-0.00138264449414098
S.D.0.034200629166372
T-STAT-0.0404274578521636
p-value0.96968972621515






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha4.54351036253031
beta0.0818241532008815
S.D.0.495224450909669
T-STAT0.165226399969913
p-value0.876779973796231
Lambda0.918175846799118

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 4.54351036253031 \tabularnewline
beta & 0.0818241532008815 \tabularnewline
S.D. & 0.495224450909669 \tabularnewline
T-STAT & 0.165226399969913 \tabularnewline
p-value & 0.876779973796231 \tabularnewline
Lambda & 0.918175846799118 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28683&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]4.54351036253031[/C][/ROW]
[ROW][C]beta[/C][C]0.0818241532008815[/C][/ROW]
[ROW][C]S.D.[/C][C]0.495224450909669[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.165226399969913[/C][/ROW]
[ROW][C]p-value[/C][C]0.876779973796231[/C][/ROW]
[ROW][C]Lambda[/C][C]0.918175846799118[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28683&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28683&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)
alpha4.54351036253031
beta0.0818241532008815
S.D.0.495224450909669
T-STAT0.165226399969913
p-value0.876779973796231
Lambda0.918175846799118


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')