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Description of Statistical Computation

Author's title

Spreidings- en gemiddeldegrafieken consumptieprijs biefstuk - Mattias Dierc...

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationThu, 21 May 2009 08:46:07 -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/2009/May/21/t1242917537a5o8tibwof38ql8.htm/, Retrieved Tue, 07 May 2024 03:04:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=40272, Retrieved Tue, 07 May 2024 03:04:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144

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] [Spreidings- en ge...] [2009-05-21 14:46:07] [cad785efdc96cabf1c219520e59eafa5] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9.370
9.330
9.310
9.260
9.350
9.380
9.430
9.270
9.290
9.270
9.290
9.310
9.330
9.350
9.340
9.470
9.630
9.620
9.630
9.500
9.550
9.580
9.610
9.570
9.610
9.650
9.620
9.650
9.960
10.030
10.030
9.720
9.750
9.770
9.780
9.820
9.840
9.900
9.940
10.120
10.520
10.570
10.570
10.120
10.050
10.140
10.170
10.200
10.200
10.350
10.430
10.570
10.820
10.900
10.830
10.650
10.570
10.610
10.630
10.710
10.720
10.770
10.790
10.920
10.900
11.000
10.990
10.910
10.880
10.870
11.000
10.990
11.030
11.040
10.990

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'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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=40272&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=40272&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=40272&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'Gwilym Jenkins' @ 72.249.127.135






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
19.321666666666670.05201980974648620.17
29.5150.1165020483627960.300000000000001
39.78250.1513049299202830.42
410.17833333333330.2518236516198420.73
510.60583333333330.2044263963267750.700000000000001
610.8950.09491623100972210.279999999999999

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 9.32166666666667 & 0.0520198097464862 & 0.17 \tabularnewline
2 & 9.515 & 0.116502048362796 & 0.300000000000001 \tabularnewline
3 & 9.7825 & 0.151304929920283 & 0.42 \tabularnewline
4 & 10.1783333333333 & 0.251823651619842 & 0.73 \tabularnewline
5 & 10.6058333333333 & 0.204426396326775 & 0.700000000000001 \tabularnewline
6 & 10.895 & 0.0949162310097221 & 0.279999999999999 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=40272&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]9.32166666666667[/C][C]0.0520198097464862[/C][C]0.17[/C][/ROW]
[ROW][C]2[/C][C]9.515[/C][C]0.116502048362796[/C][C]0.300000000000001[/C][/ROW]
[ROW][C]3[/C][C]9.7825[/C][C]0.151304929920283[/C][C]0.42[/C][/ROW]
[ROW][C]4[/C][C]10.1783333333333[/C][C]0.251823651619842[/C][C]0.73[/C][/ROW]
[ROW][C]5[/C][C]10.6058333333333[/C][C]0.204426396326775[/C][C]0.700000000000001[/C][/ROW]
[ROW][C]6[/C][C]10.895[/C][C]0.0949162310097221[/C][C]0.279999999999999[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=40272&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=40272&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
19.321666666666670.05201980974648620.17
29.5150.1165020483627960.300000000000001
39.78250.1513049299202830.42
410.17833333333330.2518236516198420.73
510.60583333333330.2044263963267750.700000000000001
610.8950.09491623100972210.279999999999999






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-0.301617870024488
beta0.0444572866104563
S.D.0.0547792062131243
T-STAT0.811572304233299
p-value0.462568262604188

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -0.301617870024488 \tabularnewline
beta & 0.0444572866104563 \tabularnewline
S.D. & 0.0547792062131243 \tabularnewline
T-STAT & 0.811572304233299 \tabularnewline
p-value & 0.462568262604188 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=40272&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.301617870024488[/C][/ROW]
[ROW][C]beta[/C][C]0.0444572866104563[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0547792062131243[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.811572304233299[/C][/ROW]
[ROW][C]p-value[/C][C]0.462568262604188[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=40272&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=40272&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-0.301617870024488
beta0.0444572866104563
S.D.0.0547792062131243
T-STAT0.811572304233299
p-value0.462568262604188






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-11.5551829900716
beta4.12086254181514
S.D.4.1301860875948
T-STAT0.997742584575631
p-value0.374871225526263
Lambda-3.12086254181514

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -11.5551829900716 \tabularnewline
beta & 4.12086254181514 \tabularnewline
S.D. & 4.1301860875948 \tabularnewline
T-STAT & 0.997742584575631 \tabularnewline
p-value & 0.374871225526263 \tabularnewline
Lambda & -3.12086254181514 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=40272&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-11.5551829900716[/C][/ROW]
[ROW][C]beta[/C][C]4.12086254181514[/C][/ROW]
[ROW][C]S.D.[/C][C]4.1301860875948[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.997742584575631[/C][/ROW]
[ROW][C]p-value[/C][C]0.374871225526263[/C][/ROW]
[ROW][C]Lambda[/C][C]-3.12086254181514[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=40272&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=40272&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-11.5551829900716
beta4.12086254181514
S.D.4.1301860875948
T-STAT0.997742584575631
p-value0.374871225526263
Lambda-3.12086254181514


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')