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Description of Statistical Computation

Author's title

Standand deviation mean plot Aantal vergunningen residentiële renovatie in ...

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationSun, 06 May 2012 16:42:25 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/May/06/t1336337003h7z4i0u8dn98ztt.htm/, Retrieved Sun, 28 Apr 2024 12:40:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=166269, Retrieved Sun, 28 Apr 2024 12:40:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W83
Estimated Impact75

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] [Standand deviatio...] [2012-05-06 20:42:25] [df2e1cb801e9c7e9e5c4a0dfd693d83a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1.974
2.037
2.259
2.550
2.549
2.738
2.228
2.533
2.475
2.260
2.158
2.253
2.670
2.449
2.620
2.205
2.589
2.706
2.352
2.478
2.316
2.295
2.110
1.944
2.202
2.036
2.434
2.297
2.354
2.650
2.555
2.477
2.268
2.510
2.015
1.994
2.271
2.289
2.333
2.795
2.332
2.799
2.294
2.415
2.473
2.236
1.970
2.318
2.108
2.064
2.519
2.298
2.187
2.746
2.364
2.512
2.224
2.209
2.186
2.303
2.381
2.432
2.913
2.392
2.532
2.709
2.387
2.609
2.399
2.184
1.839
2.056
2.151
2.155
2.463
2.155
2.679
2.367
2.052
2.547
2.466

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' @ jenkins.wessa.net

\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' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=166269&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=166269&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=166269&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' @ jenkins.wessa.net






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
12.33450.2322340120732460.764
22.39450.235338247866960.762
32.3160.2207318405997320.656
42.377083333333330.230080799378010.829
52.310.1970491401563670.682
62.402750.2861970475039881.074

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 2.3345 & 0.232234012073246 & 0.764 \tabularnewline
2 & 2.3945 & 0.23533824786696 & 0.762 \tabularnewline
3 & 2.316 & 0.220731840599732 & 0.656 \tabularnewline
4 & 2.37708333333333 & 0.23008079937801 & 0.829 \tabularnewline
5 & 2.31 & 0.197049140156367 & 0.682 \tabularnewline
6 & 2.40275 & 0.286197047503988 & 1.074 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=166269&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]2.3345[/C][C]0.232234012073246[/C][C]0.764[/C][/ROW]
[ROW][C]2[/C][C]2.3945[/C][C]0.23533824786696[/C][C]0.762[/C][/ROW]
[ROW][C]3[/C][C]2.316[/C][C]0.220731840599732[/C][C]0.656[/C][/ROW]
[ROW][C]4[/C][C]2.37708333333333[/C][C]0.23008079937801[/C][C]0.829[/C][/ROW]
[ROW][C]5[/C][C]2.31[/C][C]0.197049140156367[/C][C]0.682[/C][/ROW]
[ROW][C]6[/C][C]2.40275[/C][C]0.286197047503988[/C][C]1.074[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=166269&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=166269&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
12.33450.2322340120732460.764
22.39450.235338247866960.762
32.3160.2207318405997320.656
42.377083333333330.230080799378010.829
52.310.1970491401563670.682
62.402750.2861970475039881.074






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-1.09544581694994
beta0.564159887932501
S.D.0.222835931140709
T-STAT2.53172764842966
p-value0.0645459340559905

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -1.09544581694994 \tabularnewline
beta & 0.564159887932501 \tabularnewline
S.D. & 0.222835931140709 \tabularnewline
T-STAT & 2.53172764842966 \tabularnewline
p-value & 0.0645459340559905 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=166269&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-1.09544581694994[/C][/ROW]
[ROW][C]beta[/C][C]0.564159887932501[/C][/ROW]
[ROW][C]S.D.[/C][C]0.222835931140709[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.53172764842966[/C][/ROW]
[ROW][C]p-value[/C][C]0.0645459340559905[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=166269&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=166269&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-1.09544581694994
beta0.564159887932501
S.D.0.222835931140709
T-STAT2.53172764842966
p-value0.0645459340559905






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-6.24682862934414
beta5.58670288539843
S.D.2.12006295259302
T-STAT2.63515896005135
p-value0.0578727483998485
Lambda-4.58670288539843

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -6.24682862934414 \tabularnewline
beta & 5.58670288539843 \tabularnewline
S.D. & 2.12006295259302 \tabularnewline
T-STAT & 2.63515896005135 \tabularnewline
p-value & 0.0578727483998485 \tabularnewline
Lambda & -4.58670288539843 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=166269&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-6.24682862934414[/C][/ROW]
[ROW][C]beta[/C][C]5.58670288539843[/C][/ROW]
[ROW][C]S.D.[/C][C]2.12006295259302[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.63515896005135[/C][/ROW]
[ROW][C]p-value[/C][C]0.0578727483998485[/C][/ROW]
[ROW][C]Lambda[/C][C]-4.58670288539843[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=166269&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=166269&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-6.24682862934414
beta5.58670288539843
S.D.2.12006295259302
T-STAT2.63515896005135
p-value0.0578727483998485
Lambda-4.58670288539843


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')