Free Statistics

of Irreproducible Research!

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 computationFri, 03 Dec 2010 08:57:37 +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/03/t1291366531yww7r1216twqzbg.htm/, Retrieved Tue, 07 May 2024 12:10:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104543, Retrieved Tue, 07 May 2024 12:10:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact189
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] [] [2010-12-03 08:57:37] [8e16b01a5be2b3f7f3ad6418d9d6fd5b] [Current]
Feedback Forum
2010-12-13 10:23:07 [Stefanie Van Esbroeck] [reply
Je maakte ook hier een correcte berekening. Je interpretatie van de gegeven output is over het algemeen correct ik heb enkel mijn twijfels over je argumentatie met betrekking tot de lambda waarden. Je zegt dat deze groter is dan 1. Als ik kijk naar je berekening staat deze waarde op 1,40. Als je dat afrond zonder decimalen is dit nog altijd gelijk aan 1. In mijn workshop maakt ik wel een berekening. Dus nu weet ik niet meer zeker wat er nu correct is. In feit verteld de lambda waarde ons niet veel. Die wordt wel berekend door de software maar verder kunnen we daar geen interpretatie aan geven. Het klopt inderdaad wel dat er geen samenhang bestaat tussen de gegevens dus dit is dan weer wel correct.

Post a new message
Dataseries X:
46
62
66
59
58
61
41
27
58
70
49
59
44
36
72
45
56
54
53
35
61
52
47
51
52
63
74
45
51
64
36
30
55
64
39
40
63
45
59
55
40
64
27
28
45
57
45
69
60
56
58
50
51
53
37
22
55
70
62
58
39
49
58
47
42
62
39
40
72
70
54
65




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=104543&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=104543&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104543&T=0

As an alternative you can also use a 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
154.666666666666711.972189997378643
250.510.264679067736937
351.083333333333313.466850433789744
449.7513.712137954116742
552.666666666666712.470571418950848
653.083333333333312.191340692425433

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 54.6666666666667 & 11.9721899973786 & 43 \tabularnewline
2 & 50.5 & 10.2646790677369 & 37 \tabularnewline
3 & 51.0833333333333 & 13.4668504337897 & 44 \tabularnewline
4 & 49.75 & 13.7121379541167 & 42 \tabularnewline
5 & 52.6666666666667 & 12.4705714189508 & 48 \tabularnewline
6 & 53.0833333333333 & 12.1913406924254 & 33 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104543&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]54.6666666666667[/C][C]11.9721899973786[/C][C]43[/C][/ROW]
[ROW][C]2[/C][C]50.5[/C][C]10.2646790677369[/C][C]37[/C][/ROW]
[ROW][C]3[/C][C]51.0833333333333[/C][C]13.4668504337897[/C][C]44[/C][/ROW]
[ROW][C]4[/C][C]49.75[/C][C]13.7121379541167[/C][C]42[/C][/ROW]
[ROW][C]5[/C][C]52.6666666666667[/C][C]12.4705714189508[/C][C]48[/C][/ROW]
[ROW][C]6[/C][C]53.0833333333333[/C][C]12.1913406924254[/C][C]33[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104543&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104543&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
154.666666666666711.972189997378643
250.510.264679067736937
351.083333333333313.466850433789744
449.7513.712137954116742
552.666666666666712.470571418950848
653.083333333333312.191340692425433







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha18.6919922134068
beta-0.122130501093962
S.D.0.33073593097988
T-STAT-0.369268923192358
p-value0.730644321403998

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 18.6919922134068 \tabularnewline
beta & -0.122130501093962 \tabularnewline
S.D. & 0.33073593097988 \tabularnewline
T-STAT & -0.369268923192358 \tabularnewline
p-value & 0.730644321403998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104543&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]18.6919922134068[/C][/ROW]
[ROW][C]beta[/C][C]-0.122130501093962[/C][/ROW]
[ROW][C]S.D.[/C][C]0.33073593097988[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.369268923192358[/C][/ROW]
[ROW][C]p-value[/C][C]0.730644321403998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104543&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104543&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha18.6919922134068
beta-0.122130501093962
S.D.0.33073593097988
T-STAT-0.369268923192358
p-value0.730644321403998







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha4.09200505081258
beta-0.400772512903992
S.D.1.45747244856086
T-STAT-0.274977762564036
p-value0.79695219984824
Lambda1.40077251290399

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 4.09200505081258 \tabularnewline
beta & -0.400772512903992 \tabularnewline
S.D. & 1.45747244856086 \tabularnewline
T-STAT & -0.274977762564036 \tabularnewline
p-value & 0.79695219984824 \tabularnewline
Lambda & 1.40077251290399 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104543&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]4.09200505081258[/C][/ROW]
[ROW][C]beta[/C][C]-0.400772512903992[/C][/ROW]
[ROW][C]S.D.[/C][C]1.45747244856086[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.274977762564036[/C][/ROW]
[ROW][C]p-value[/C][C]0.79695219984824[/C][/ROW]
[ROW][C]Lambda[/C][C]1.40077251290399[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104543&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104543&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha4.09200505081258
beta-0.400772512903992
S.D.1.45747244856086
T-STAT-0.274977762564036
p-value0.79695219984824
Lambda1.40077251290399



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')