FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationMon, 22 Dec 2008 02:10:36 -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/22/t1229937085jb4ygtf9r33i286.htm/, Retrieved Sun, 12 May 2024 21:17:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=35949, Retrieved Sun, 12 May 2024 21:17:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact191

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Standard Deviation-Mean Plot] [SMP van de gemidd...] [2008-12-22 08:52:45] [74be16979710d4c4e7c6647856088456]
F    D    [Standard Deviation-Mean Plot] [VRM BEL-20] [2008-12-22 09:10:36] [7ed4ec9f8cdf7df79ef87b9dc09dff20] [Current]
Feedback Forum
2009-01-01 22:00:46 [Kenny Simons] [reply] 
Ook hier is de p-value groter als 5%, dus kunnen we deze lambda waarde niet aanvaarden. Hierdoor neem je 1 als lambda waarde bij het berekenen van het ARIMA model.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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
2962.34
2197.82
2014.45

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=35949&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=35949&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35949&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
12349.0125108.127645979185243.53
22445.7727.517457489140765.21
32687.3775151.316000117855351.43
43022.522586.1159467907463184.780000000000
53109.892542.0123381678922100.430000000000
63298.3746.4392097262646106.83
73723.055184.043475751429423.79
83742.075139.316746660263325.390000000000
94042.66154.565965852771337.48
104398.60593.4718961328306211.75
114618.117557.7445664832053134.12
124259.855140.606035550873330.049999999999
133839.1375198.459170674978442.28
143548.9125377.465810493701825.02
152555.41525.2905795842911032.58

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 2349.0125 & 108.127645979185 & 243.53 \tabularnewline
2 & 2445.77 & 27.5174574891407 & 65.21 \tabularnewline
3 & 2687.3775 & 151.316000117855 & 351.43 \tabularnewline
4 & 3022.5225 & 86.1159467907463 & 184.780000000000 \tabularnewline
5 & 3109.8925 & 42.0123381678922 & 100.430000000000 \tabularnewline
6 & 3298.37 & 46.4392097262646 & 106.83 \tabularnewline
7 & 3723.055 & 184.043475751429 & 423.79 \tabularnewline
8 & 3742.075 & 139.316746660263 & 325.390000000000 \tabularnewline
9 & 4042.66 & 154.565965852771 & 337.48 \tabularnewline
10 & 4398.605 & 93.4718961328306 & 211.75 \tabularnewline
11 & 4618.1175 & 57.7445664832053 & 134.12 \tabularnewline
12 & 4259.855 & 140.606035550873 & 330.049999999999 \tabularnewline
13 & 3839.1375 & 198.459170674978 & 442.28 \tabularnewline
14 & 3548.9125 & 377.465810493701 & 825.02 \tabularnewline
15 & 2555.41 & 525.290579584291 & 1032.58 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35949&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]2349.0125[/C][C]108.127645979185[/C][C]243.53[/C][/ROW]
[ROW][C]2[/C][C]2445.77[/C][C]27.5174574891407[/C][C]65.21[/C][/ROW]
[ROW][C]3[/C][C]2687.3775[/C][C]151.316000117855[/C][C]351.43[/C][/ROW]
[ROW][C]4[/C][C]3022.5225[/C][C]86.1159467907463[/C][C]184.780000000000[/C][/ROW]
[ROW][C]5[/C][C]3109.8925[/C][C]42.0123381678922[/C][C]100.430000000000[/C][/ROW]
[ROW][C]6[/C][C]3298.37[/C][C]46.4392097262646[/C][C]106.83[/C][/ROW]
[ROW][C]7[/C][C]3723.055[/C][C]184.043475751429[/C][C]423.79[/C][/ROW]
[ROW][C]8[/C][C]3742.075[/C][C]139.316746660263[/C][C]325.390000000000[/C][/ROW]
[ROW][C]9[/C][C]4042.66[/C][C]154.565965852771[/C][C]337.48[/C][/ROW]
[ROW][C]10[/C][C]4398.605[/C][C]93.4718961328306[/C][C]211.75[/C][/ROW]
[ROW][C]11[/C][C]4618.1175[/C][C]57.7445664832053[/C][C]134.12[/C][/ROW]
[ROW][C]12[/C][C]4259.855[/C][C]140.606035550873[/C][C]330.049999999999[/C][/ROW]
[ROW][C]13[/C][C]3839.1375[/C][C]198.459170674978[/C][C]442.28[/C][/ROW]
[ROW][C]14[/C][C]3548.9125[/C][C]377.465810493701[/C][C]825.02[/C][/ROW]
[ROW][C]15[/C][C]2555.41[/C][C]525.290579584291[/C][C]1032.58[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35949&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35949&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
12349.0125108.127645979185243.53
22445.7727.517457489140765.21
32687.3775151.316000117855351.43
43022.522586.1159467907463184.780000000000
53109.892542.0123381678922100.430000000000
63298.3746.4392097262646106.83
73723.055184.043475751429423.79
83742.075139.316746660263325.390000000000
94042.66154.565965852771337.48
104398.60593.4718961328306211.75
114618.117557.7445664832053134.12
124259.855140.606035550873330.049999999999
133839.1375198.459170674978442.28
143548.9125377.465810493701825.02
152555.41525.2905795842911032.58






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha249.885570581555
beta-0.0274161412528813
S.D.0.0500902507023579
T-STAT-0.547334877914491
p-value0.593421191121655

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 249.885570581555 \tabularnewline
beta & -0.0274161412528813 \tabularnewline
S.D. & 0.0500902507023579 \tabularnewline
T-STAT & -0.547334877914491 \tabularnewline
p-value & 0.593421191121655 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35949&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]249.885570581555[/C][/ROW]
[ROW][C]beta[/C][C]-0.0274161412528813[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0500902507023579[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.547334877914491[/C][/ROW]
[ROW][C]p-value[/C][C]0.593421191121655[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35949&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35949&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)
alpha249.885570581555
beta-0.0274161412528813
S.D.0.0500902507023579
T-STAT-0.547334877914491
p-value0.593421191121655






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha2.85560064345720
beta0.233404956858485
S.D.1.00615897441219
T-STAT0.231976221247584
p-value0.820168206701997
Lambda0.766595043141515

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 2.85560064345720 \tabularnewline
beta & 0.233404956858485 \tabularnewline
S.D. & 1.00615897441219 \tabularnewline
T-STAT & 0.231976221247584 \tabularnewline
p-value & 0.820168206701997 \tabularnewline
Lambda & 0.766595043141515 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35949&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]2.85560064345720[/C][/ROW]
[ROW][C]beta[/C][C]0.233404956858485[/C][/ROW]
[ROW][C]S.D.[/C][C]1.00615897441219[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.231976221247584[/C][/ROW]
[ROW][C]p-value[/C][C]0.820168206701997[/C][/ROW]
[ROW][C]Lambda[/C][C]0.766595043141515[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35949&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35949&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)
alpha2.85560064345720
beta0.233404956858485
S.D.1.00615897441219
T-STAT0.231976221247584
p-value0.820168206701997
Lambda0.766595043141515


Figures (Output of Computation)

Input Parameters & R Code

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')