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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 computationThu, 16 Dec 2010 17:08:45 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/16/t129251924211fskywv137zkwm.htm/, Retrieved Fri, 03 May 2024 05:23:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=111100, Retrieved Fri, 03 May 2024 05:23:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact147

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
F   PD      [Standard Deviation-Mean Plot] [Aantal openstaand...] [2010-12-16 17:08:45] [f0b33ae54e73edcd25a3e2f31270d1c9] [Current]
Feedback Forum
2010-12-20 08:32:24 [] [reply] 
Deze hoge p-waarde kan het gevolg zijn van uitvallers. Dit zou eventueel nader onderzocht moeten worden.
Indien er wel degelijk uitvallers zijn, beïnvloeden deze de p-waarde en daarmee ook de mogelijke lambda waarde.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
27.951
29.781
32.914
33.488
35.652
36.488
35.387
35.676
34.844
32.447
31.068
29.010
29.812
30.951
32.974
32.936
34.012
32.946
31.948
30.599
27.691
25.073
23.406
22.248
22.896
25.317
26.558
26.471
27.543
26.198
24.725
25.005
23.462
20.780
19.815
19.761
21.454
23.899
24.939
23.580
24.562
24.696
23.785
23.812
21.917
19.713
19.282
18.788
21.453
24.482
27.474
27.264
27.349
30.632
29.429
30.084
26.290
24.379
23.335
21.346
21.106
24.514
28.353
30.805
31.348
34.556
33.855
34.787
32.529
29.998
29.257
28.155
30.466
35.704
39.327
39.351
42.234
43.630
43.722
43.121
37.985
37.135
34.646
33.026
35.087
38.846
42.013
43.908
42.868
44.423
44.167
43.636
44.382
42.142
43.452
36.912
42.413
45.344
44.873
47.510
49.554
47.369
45.998
48.140
48.441
44.928
40.454
38.661
37.246
36.843
36.424
37.594
38.144
38.737
34.560
36.080
33.508
35.462
33.374
32.110
35.533
35.532
37.903
36.763
40.399
44.164
44.496
43.110
43.880
43.930
44.327

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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111100&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111100&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111100&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
132.89216666666672.882190260369658.537
229.54966666666674.0235740632457911.764
324.044252.707563585251847.782
422.53558333333332.232809052550516.151
526.12641666666673.158597717006529.286
629.93858333333334.0811913915231213.681
738.362254.3627787668161513.256
841.81966666666673.143167925208689.336
945.30708333333333.3288943689605110.893
1035.84016666666672.075235050578976.627

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 32.8921666666667 & 2.88219026036965 & 8.537 \tabularnewline
2 & 29.5496666666667 & 4.02357406324579 & 11.764 \tabularnewline
3 & 24.04425 & 2.70756358525184 & 7.782 \tabularnewline
4 & 22.5355833333333 & 2.23280905255051 & 6.151 \tabularnewline
5 & 26.1264166666667 & 3.15859771700652 & 9.286 \tabularnewline
6 & 29.9385833333333 & 4.08119139152312 & 13.681 \tabularnewline
7 & 38.36225 & 4.36277876681615 & 13.256 \tabularnewline
8 & 41.8196666666667 & 3.14316792520868 & 9.336 \tabularnewline
9 & 45.3070833333333 & 3.32889436896051 & 10.893 \tabularnewline
10 & 35.8401666666667 & 2.07523505057897 & 6.627 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111100&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]32.8921666666667[/C][C]2.88219026036965[/C][C]8.537[/C][/ROW]
[ROW][C]2[/C][C]29.5496666666667[/C][C]4.02357406324579[/C][C]11.764[/C][/ROW]
[ROW][C]3[/C][C]24.04425[/C][C]2.70756358525184[/C][C]7.782[/C][/ROW]
[ROW][C]4[/C][C]22.5355833333333[/C][C]2.23280905255051[/C][C]6.151[/C][/ROW]
[ROW][C]5[/C][C]26.1264166666667[/C][C]3.15859771700652[/C][C]9.286[/C][/ROW]
[ROW][C]6[/C][C]29.9385833333333[/C][C]4.08119139152312[/C][C]13.681[/C][/ROW]
[ROW][C]7[/C][C]38.36225[/C][C]4.36277876681615[/C][C]13.256[/C][/ROW]
[ROW][C]8[/C][C]41.8196666666667[/C][C]3.14316792520868[/C][C]9.336[/C][/ROW]
[ROW][C]9[/C][C]45.3070833333333[/C][C]3.32889436896051[/C][C]10.893[/C][/ROW]
[ROW][C]10[/C][C]35.8401666666667[/C][C]2.07523505057897[/C][C]6.627[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111100&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111100&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
132.89216666666672.882190260369658.537
229.54966666666674.0235740632457911.764
324.044252.707563585251847.782
422.53558333333332.232809052550516.151
526.12641666666673.158597717006529.286
629.93858333333334.0811913915231213.681
738.362254.3627787668161513.256
841.81966666666673.143167925208689.336
945.30708333333333.3288943689605110.893
1035.84016666666672.075235050578976.627






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha2.36114089064484
beta0.0256868460988566
S.D.0.0346625197792316
T-STAT0.741055360731367
p-value0.479844410656325

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 2.36114089064484 \tabularnewline
beta & 0.0256868460988566 \tabularnewline
S.D. & 0.0346625197792316 \tabularnewline
T-STAT & 0.741055360731367 \tabularnewline
p-value & 0.479844410656325 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111100&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]2.36114089064484[/C][/ROW]
[ROW][C]beta[/C][C]0.0256868460988566[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0346625197792316[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.741055360731367[/C][/ROW]
[ROW][C]p-value[/C][C]0.479844410656325[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111100&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111100&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)
alpha2.36114089064484
beta0.0256868460988566
S.D.0.0346625197792316
T-STAT0.741055360731367
p-value0.479844410656325






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha0.0596053476489975
beta0.310943262969016
S.D.0.358842841147866
T-STAT0.866516556312984
p-value0.411440689368698
Lambda0.689056737030984

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 0.0596053476489975 \tabularnewline
beta & 0.310943262969016 \tabularnewline
S.D. & 0.358842841147866 \tabularnewline
T-STAT & 0.866516556312984 \tabularnewline
p-value & 0.411440689368698 \tabularnewline
Lambda & 0.689056737030984 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111100&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.0596053476489975[/C][/ROW]
[ROW][C]beta[/C][C]0.310943262969016[/C][/ROW]
[ROW][C]S.D.[/C][C]0.358842841147866[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.866516556312984[/C][/ROW]
[ROW][C]p-value[/C][C]0.411440689368698[/C][/ROW]
[ROW][C]Lambda[/C][C]0.689056737030984[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111100&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111100&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)
alpha0.0596053476489975
beta0.310943262969016
S.D.0.358842841147866
T-STAT0.866516556312984
p-value0.411440689368698
Lambda0.689056737030984


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')