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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 computationMon, 01 Dec 2008 14:45:03 -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/01/t1228167959g9h8uba1ncnabqw.htm/, Retrieved Sun, 05 May 2024 10:39:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27436, Retrieved Sun, 05 May 2024 10:39:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact212

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD  [Standard Deviation-Mean Plot] [] [2008-12-01 21:10:18] [cb714085b233acee8e8acd879ea442b6]
F   PD      [Standard Deviation-Mean Plot] [Q8] [2008-12-01 21:45:03] [787873b6436f665b5b192a0bdb2e43c9] [Current]
Feedback Forum
2008-12-08 10:06:59 [Joris Deboel] [reply] 
correct
2008-12-18 16:40:35 [] [reply] 
De student had om de p,P,q en Q te vinden gebruik moeten maken van het backward selection model. Om te weten om welk proces het gaat moet men de eerste vijf observaties bestuderen bij de autocorrelatie en zien welk verloop deze observaties volgen.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13.92
13.22
13.31
12.91
13.19
12.92
13.43
13.72
13.97
14.91
14.46
14.12
14.23
15.04
14.80
14.49
15.14
14.34
15.12
15.14
14.34
14.36
14.91
15.56
16.50
15.57
15.14
15.19
15.07
14.48
14.27
14.72
14.65
14.38
13.95
14.85
14.87
14.83
15.03
15.47
16.21
16.55
17.04
17.22
17.47
17.75
17.84
18.47
18.38
18.55
18.39
18.88
20.21
19.67
20.09
18.78
19.74
20.64
20.34
21.75
22.10
22.81
22.91
22.46
21.78
25.05
23.70
23.02
24.34
24.15
25.85
26.42
26.54
26.36
26.99
27.52
26.63
26.26
24.86
26.84
26.57
24.67
27.24
27.77
27.61
27.27
28.46
26.97
29.95
29.88
29.67
31.19
30.24
30.03
31.02
30.45
31.70
32.10
32.32
32.18
33.43
33.07
35.32
35.17
35.29
37.89
38.32
37.07
39.77
39.20
40.46
44.95
41.69
41.88
45.86

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=27436&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=27436&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27436&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
113.67333333333330.6237180792828142
214.78916666666670.4282407634907631.33
314.89750.675750424612252.55
416.56251.268400030102353.64
519.61833333333331.050115433770223.37
623.71583333333331.480635357070094.64
726.52083333333330.9385913849374433.1
829.3951.451284697337944.22
934.48833333333332.361774499794346.62

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 13.6733333333333 & 0.623718079282814 & 2 \tabularnewline
2 & 14.7891666666667 & 0.428240763490763 & 1.33 \tabularnewline
3 & 14.8975 & 0.67575042461225 & 2.55 \tabularnewline
4 & 16.5625 & 1.26840003010235 & 3.64 \tabularnewline
5 & 19.6183333333333 & 1.05011543377022 & 3.37 \tabularnewline
6 & 23.7158333333333 & 1.48063535707009 & 4.64 \tabularnewline
7 & 26.5208333333333 & 0.938591384937443 & 3.1 \tabularnewline
8 & 29.395 & 1.45128469733794 & 4.22 \tabularnewline
9 & 34.4883333333333 & 2.36177449979434 & 6.62 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27436&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]13.6733333333333[/C][C]0.623718079282814[/C][C]2[/C][/ROW]
[ROW][C]2[/C][C]14.7891666666667[/C][C]0.428240763490763[/C][C]1.33[/C][/ROW]
[ROW][C]3[/C][C]14.8975[/C][C]0.67575042461225[/C][C]2.55[/C][/ROW]
[ROW][C]4[/C][C]16.5625[/C][C]1.26840003010235[/C][C]3.64[/C][/ROW]
[ROW][C]5[/C][C]19.6183333333333[/C][C]1.05011543377022[/C][C]3.37[/C][/ROW]
[ROW][C]6[/C][C]23.7158333333333[/C][C]1.48063535707009[/C][C]4.64[/C][/ROW]
[ROW][C]7[/C][C]26.5208333333333[/C][C]0.938591384937443[/C][C]3.1[/C][/ROW]
[ROW][C]8[/C][C]29.395[/C][C]1.45128469733794[/C][C]4.22[/C][/ROW]
[ROW][C]9[/C][C]34.4883333333333[/C][C]2.36177449979434[/C][C]6.62[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27436&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27436&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
113.67333333333330.6237180792828142
214.78916666666670.4282407634907631.33
314.89750.675750424612252.55
416.56251.268400030102353.64
519.61833333333331.050115433770223.37
623.71583333333331.480635357070094.64
726.52083333333330.9385913849374433.1
829.3951.451284697337944.22
934.48833333333332.361774499794346.62






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-0.303450891241569
beta0.0671770768908134
S.D.0.0159181179598596
T-STAT4.22016453579579
p-value0.00393580243372192

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -0.303450891241569 \tabularnewline
beta & 0.0671770768908134 \tabularnewline
S.D. & 0.0159181179598596 \tabularnewline
T-STAT & 4.22016453579579 \tabularnewline
p-value & 0.00393580243372192 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27436&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.303450891241569[/C][/ROW]
[ROW][C]beta[/C][C]0.0671770768908134[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0159181179598596[/C][/ROW]
[ROW][C]T-STAT[/C][C]4.22016453579579[/C][/ROW]
[ROW][C]p-value[/C][C]0.00393580243372192[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27436&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27436&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-0.303450891241569
beta0.0671770768908134
S.D.0.0159181179598596
T-STAT4.22016453579579
p-value0.00393580243372192






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-3.85006732309527
beta1.28080256473762
S.D.0.331516102436890
T-STAT3.86347014616414
p-value0.00618367321136711
Lambda-0.280802564737615

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -3.85006732309527 \tabularnewline
beta & 1.28080256473762 \tabularnewline
S.D. & 0.331516102436890 \tabularnewline
T-STAT & 3.86347014616414 \tabularnewline
p-value & 0.00618367321136711 \tabularnewline
Lambda & -0.280802564737615 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27436&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-3.85006732309527[/C][/ROW]
[ROW][C]beta[/C][C]1.28080256473762[/C][/ROW]
[ROW][C]S.D.[/C][C]0.331516102436890[/C][/ROW]
[ROW][C]T-STAT[/C][C]3.86347014616414[/C][/ROW]
[ROW][C]p-value[/C][C]0.00618367321136711[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.280802564737615[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27436&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27436&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-3.85006732309527
beta1.28080256473762
S.D.0.331516102436890
T-STAT3.86347014616414
p-value0.00618367321136711
Lambda-0.280802564737615


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')