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Description of Statistical Computation

Author's title

Opgave 8 standard deviation mean plot - gem consumptieprijs tomaten - Bram ...

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationWed, 30 Jul 2008 04:12:27 -0600
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/Jul/30/t1217412821kx7e1qzfi1hac5d.htm/, Retrieved Fri, 24 May 2024 16:04:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=13886, Retrieved Fri, 24 May 2024 16:04:43 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact293

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] [Opgave 8 standard...] [2008-07-30 10:12:27] [f1ad3272590ff3a9e1233970549442f0] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2.1300
1.8700
2.2300
3.0000
2.1200
1.6000
1.1700
1.0200
1.2200
1.8000
2.1300
2.2100
2.3800
1.9900
1.8200
2.4700
1.9400
1.3900
1.1100
0.9700
1.3800
2.3900
1.8800
2.1100
2.1100
2.1700
2.5400
3.1300
2.2500
1.3900
1.3600
1.3300
1.6000
1.9500
2.2300
2.5300
2.3600
1.9500
2.1600
2.7600
2.0900
1.4900
1.1700
1.3000
1.2600
2.1700
2.0300
2.1800
2.6100
2.5800
3.8600
3.8100
2.4100
1.4700
1.3300
1.3800
1.5700
2.6000
2.1800
2.3600
2.2400
2.4100
2.5100
2.9800
1.8700
1.9000
1.4700
1.4500
2.7100
2.9000
2.1100
2.1800
2.2400
2.0500
2.4200
2.7700
1.9900
1.4700
1.0900
0.9300
1.3200
2.0300
2.0400
2.7800
2.8000
3.0300
3.1100
2.7500
2.7800
1.7600
1.2900
1.2800
1.4300
1.7100
1.8900
1.8400

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=13886&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]8 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=13886&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=13886&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 time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
12.30750.4859612467402451.13
21.47750.4938538920234071.1
31.840.4498147766951780.99
42.1650.3103224129836580.65
51.35250.4288259165053650.97
61.940.4276291227999641.01
72.48750.4686416541452541.02
81.58250.4456736474147870.92
92.07750.3967681942898140.93
102.30750.3450.81
111.51250.4068066698896010.92
121.910.4387102308661910.92
133.2150.7163099887618491.28
141.64750.511623233770061.08
152.17750.4400284081738361.03
162.5350.3169121855235820.74
171.67250.2458149710656370.45
182.4750.3899145205469180.79
192.370.3064854537059360.72
201.370.4713102304568971.06
212.04250.5961193951997651.46
222.92250.1746186320719150.36
231.77750.7048581417561981.5
241.71750.2061350689879490.46

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 2.3075 & 0.485961246740245 & 1.13 \tabularnewline
2 & 1.4775 & 0.493853892023407 & 1.1 \tabularnewline
3 & 1.84 & 0.449814776695178 & 0.99 \tabularnewline
4 & 2.165 & 0.310322412983658 & 0.65 \tabularnewline
5 & 1.3525 & 0.428825916505365 & 0.97 \tabularnewline
6 & 1.94 & 0.427629122799964 & 1.01 \tabularnewline
7 & 2.4875 & 0.468641654145254 & 1.02 \tabularnewline
8 & 1.5825 & 0.445673647414787 & 0.92 \tabularnewline
9 & 2.0775 & 0.396768194289814 & 0.93 \tabularnewline
10 & 2.3075 & 0.345 & 0.81 \tabularnewline
11 & 1.5125 & 0.406806669889601 & 0.92 \tabularnewline
12 & 1.91 & 0.438710230866191 & 0.92 \tabularnewline
13 & 3.215 & 0.716309988761849 & 1.28 \tabularnewline
14 & 1.6475 & 0.51162323377006 & 1.08 \tabularnewline
15 & 2.1775 & 0.440028408173836 & 1.03 \tabularnewline
16 & 2.535 & 0.316912185523582 & 0.74 \tabularnewline
17 & 1.6725 & 0.245814971065637 & 0.45 \tabularnewline
18 & 2.475 & 0.389914520546918 & 0.79 \tabularnewline
19 & 2.37 & 0.306485453705936 & 0.72 \tabularnewline
20 & 1.37 & 0.471310230456897 & 1.06 \tabularnewline
21 & 2.0425 & 0.596119395199765 & 1.46 \tabularnewline
22 & 2.9225 & 0.174618632071915 & 0.36 \tabularnewline
23 & 1.7775 & 0.704858141756198 & 1.5 \tabularnewline
24 & 1.7175 & 0.206135068987949 & 0.46 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=13886&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]2.3075[/C][C]0.485961246740245[/C][C]1.13[/C][/ROW]
[ROW][C]2[/C][C]1.4775[/C][C]0.493853892023407[/C][C]1.1[/C][/ROW]
[ROW][C]3[/C][C]1.84[/C][C]0.449814776695178[/C][C]0.99[/C][/ROW]
[ROW][C]4[/C][C]2.165[/C][C]0.310322412983658[/C][C]0.65[/C][/ROW]
[ROW][C]5[/C][C]1.3525[/C][C]0.428825916505365[/C][C]0.97[/C][/ROW]
[ROW][C]6[/C][C]1.94[/C][C]0.427629122799964[/C][C]1.01[/C][/ROW]
[ROW][C]7[/C][C]2.4875[/C][C]0.468641654145254[/C][C]1.02[/C][/ROW]
[ROW][C]8[/C][C]1.5825[/C][C]0.445673647414787[/C][C]0.92[/C][/ROW]
[ROW][C]9[/C][C]2.0775[/C][C]0.396768194289814[/C][C]0.93[/C][/ROW]
[ROW][C]10[/C][C]2.3075[/C][C]0.345[/C][C]0.81[/C][/ROW]
[ROW][C]11[/C][C]1.5125[/C][C]0.406806669889601[/C][C]0.92[/C][/ROW]
[ROW][C]12[/C][C]1.91[/C][C]0.438710230866191[/C][C]0.92[/C][/ROW]
[ROW][C]13[/C][C]3.215[/C][C]0.716309988761849[/C][C]1.28[/C][/ROW]
[ROW][C]14[/C][C]1.6475[/C][C]0.51162323377006[/C][C]1.08[/C][/ROW]
[ROW][C]15[/C][C]2.1775[/C][C]0.440028408173836[/C][C]1.03[/C][/ROW]
[ROW][C]16[/C][C]2.535[/C][C]0.316912185523582[/C][C]0.74[/C][/ROW]
[ROW][C]17[/C][C]1.6725[/C][C]0.245814971065637[/C][C]0.45[/C][/ROW]
[ROW][C]18[/C][C]2.475[/C][C]0.389914520546918[/C][C]0.79[/C][/ROW]
[ROW][C]19[/C][C]2.37[/C][C]0.306485453705936[/C][C]0.72[/C][/ROW]
[ROW][C]20[/C][C]1.37[/C][C]0.471310230456897[/C][C]1.06[/C][/ROW]
[ROW][C]21[/C][C]2.0425[/C][C]0.596119395199765[/C][C]1.46[/C][/ROW]
[ROW][C]22[/C][C]2.9225[/C][C]0.174618632071915[/C][C]0.36[/C][/ROW]
[ROW][C]23[/C][C]1.7775[/C][C]0.704858141756198[/C][C]1.5[/C][/ROW]
[ROW][C]24[/C][C]1.7175[/C][C]0.206135068987949[/C][C]0.46[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=13886&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=13886&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
12.30750.4859612467402451.13
21.47750.4938538920234071.1
31.840.4498147766951780.99
42.1650.3103224129836580.65
51.35250.4288259165053650.97
61.940.4276291227999641.01
72.48750.4686416541452541.02
81.58250.4456736474147870.92
92.07750.3967681942898140.93
102.30750.3450.81
111.51250.4068066698896010.92
121.910.4387102308661910.92
133.2150.7163099887618491.28
141.64750.511623233770061.08
152.17750.4400284081738361.03
162.5350.3169121855235820.74
171.67250.2458149710656370.45
182.4750.3899145205469180.79
192.370.3064854537059360.72
201.370.4713102304568971.06
212.04250.5961193951997651.46
222.92250.1746186320719150.36
231.77750.7048581417561981.5
241.71750.2061350689879490.46






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha0.4375884156614
beta-0.00662781121054747
S.D.0.0586661195939041
T-STAT-0.112975108229864
p-value0.911074861991806

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 0.4375884156614 \tabularnewline
beta & -0.00662781121054747 \tabularnewline
S.D. & 0.0586661195939041 \tabularnewline
T-STAT & -0.112975108229864 \tabularnewline
p-value & 0.911074861991806 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=13886&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.4375884156614[/C][/ROW]
[ROW][C]beta[/C][C]-0.00662781121054747[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0586661195939041[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.112975108229864[/C][/ROW]
[ROW][C]p-value[/C][C]0.911074861991806[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=13886&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=13886&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)
alpha0.4375884156614
beta-0.00662781121054747
S.D.0.0586661195939041
T-STAT-0.112975108229864
p-value0.911074861991806






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-0.781409876562785
beta-0.186143635102473
S.D.0.308712739274593
T-STAT-0.602967132292205
p-value0.552696349917031
Lambda1.18614363510247

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -0.781409876562785 \tabularnewline
beta & -0.186143635102473 \tabularnewline
S.D. & 0.308712739274593 \tabularnewline
T-STAT & -0.602967132292205 \tabularnewline
p-value & 0.552696349917031 \tabularnewline
Lambda & 1.18614363510247 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=13886&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.781409876562785[/C][/ROW]
[ROW][C]beta[/C][C]-0.186143635102473[/C][/ROW]
[ROW][C]S.D.[/C][C]0.308712739274593[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.602967132292205[/C][/ROW]
[ROW][C]p-value[/C][C]0.552696349917031[/C][/ROW]
[ROW][C]Lambda[/C][C]1.18614363510247[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=13886&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=13886&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-0.781409876562785
beta-0.186143635102473
S.D.0.308712739274593
T-STAT-0.602967132292205
p-value0.552696349917031
Lambda1.18614363510247


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')