Free Statistics

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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 computationMon, 08 Dec 2008 12:16:12 -0700
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/Dec/08/t1228763821lmmzhjudivflvov.htm/, Retrieved Thu, 16 May 2024 04:42:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=30767, Retrieved Thu, 16 May 2024 04:42:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact168
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]
F RMP   [Standard Deviation-Mean Plot] [step 1] [2008-12-08 11:54:47] [6bf01ed8d6668535fdab898b5820a5bc]
F    D      [Standard Deviation-Mean Plot] [step 1] [2008-12-08 19:16:12] [db9a5fd0f9c3e1245d8075d8bb09236d] [Current]
Feedback Forum
2008-12-14 14:11:41 [Jeroen Michel] [reply
Ook hier stelt de student duidelijk hoe de data, grafieken, en tabellen moeten worden afgelezen. Op die manier kan de lezer van dit werk meteen de resultaten interpreteren. Ook hier hangt dus een zeer uitgebreide analyse aan vast.

Post a new message
Dataseries X:
9097,4
12639,8
13040,1
11687,3
11191,7
11391,9
11793,1
13933,2
12778,1
11810,3
13698,4
11956,6
10723,8
13938,9
13979,8
13807,4
12973,9
12509,8
12934,1
14908,3
13772,1
13012,6
14049,9
11816,5
11593,2
14466,2
13615,9
14733,9
13880,7
13527,5
13584
16170,2
13260,6
14741,9
15486,5
13154,5
12621,2
15031,6
15452,4
15428
13105,9
14716,8
14180
16202,2
14392,4
15140,6
15960,1
14351,3
13230,2
15202,1
17157,3
16159,1
13405,7
17224,7
17338,4
17370,6
18817,8
16593,2
17979,5
17015,2




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30767&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]3 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=30767&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30767&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 time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
112084.8251288.365874858544835.8
213202.25833333331134.973352877694184.5
314017.9251199.784385269444577
414715.20833333331069.890136635393581
516457.81666666671713.555984088665587.6

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 12084.825 & 1288.36587485854 & 4835.8 \tabularnewline
2 & 13202.2583333333 & 1134.97335287769 & 4184.5 \tabularnewline
3 & 14017.925 & 1199.78438526944 & 4577 \tabularnewline
4 & 14715.2083333333 & 1069.89013663539 & 3581 \tabularnewline
5 & 16457.8166666667 & 1713.55598408866 & 5587.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30767&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]12084.825[/C][C]1288.36587485854[/C][C]4835.8[/C][/ROW]
[ROW][C]2[/C][C]13202.2583333333[/C][C]1134.97335287769[/C][C]4184.5[/C][/ROW]
[ROW][C]3[/C][C]14017.925[/C][C]1199.78438526944[/C][C]4577[/C][/ROW]
[ROW][C]4[/C][C]14715.2083333333[/C][C]1069.89013663539[/C][C]3581[/C][/ROW]
[ROW][C]5[/C][C]16457.8166666667[/C][C]1713.55598408866[/C][C]5587.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30767&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30767&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
112084.8251288.365874858544835.8
213202.25833333331134.973352877694184.5
314017.9251199.784385269444577
414715.20833333331069.890136635393581
516457.81666666671713.555984088665587.6







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-39.3355970884806
beta0.0936922812238724
S.D.0.0712692209887278
T-STAT1.31462474156538
p-value0.280104213640506

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -39.3355970884806 \tabularnewline
beta & 0.0936922812238724 \tabularnewline
S.D. & 0.0712692209887278 \tabularnewline
T-STAT & 1.31462474156538 \tabularnewline
p-value & 0.280104213640506 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30767&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-39.3355970884806[/C][/ROW]
[ROW][C]beta[/C][C]0.0936922812238724[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0712692209887278[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.31462474156538[/C][/ROW]
[ROW][C]p-value[/C][C]0.280104213640506[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30767&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30767&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)
alpha-39.3355970884806
beta0.0936922812238724
S.D.0.0712692209887278
T-STAT1.31462474156538
p-value0.280104213640506







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-0.742800675652514
beta0.825724674747917
S.D.0.781561447593098
T-STAT1.05650640431514
p-value0.36829068210048
Lambda0.174275325252083

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -0.742800675652514 \tabularnewline
beta & 0.825724674747917 \tabularnewline
S.D. & 0.781561447593098 \tabularnewline
T-STAT & 1.05650640431514 \tabularnewline
p-value & 0.36829068210048 \tabularnewline
Lambda & 0.174275325252083 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30767&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.742800675652514[/C][/ROW]
[ROW][C]beta[/C][C]0.825724674747917[/C][/ROW]
[ROW][C]S.D.[/C][C]0.781561447593098[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.05650640431514[/C][/ROW]
[ROW][C]p-value[/C][C]0.36829068210048[/C][/ROW]
[ROW][C]Lambda[/C][C]0.174275325252083[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30767&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30767&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)
alpha-0.742800675652514
beta0.825724674747917
S.D.0.781561447593098
T-STAT1.05650640431514
p-value0.36829068210048
Lambda0.174275325252083



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')