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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_smp.wasp

Title produced by software

Standard Deviation-Mean Plot

Date of computation

Tue, 20 Dec 2016 22:14:42 +0100

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/20/t1482268513afelgcejwvfufbs.htm/, Retrieved Fri, 01 Nov 2024 03:46:10 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=301809, Retrieved Fri, 01 Nov 2024 03:46:10 +0000

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Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Standard Deviation-Mean Plot] [standard deviatio...] [2016-12-20 21:14:42] [2d1dd91c3b5ba64567b1d6b2c9fe9017] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301809&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

1 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=301809&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301809&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	Big Analytics Cloud Computing Center

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	5761.425	37.3259851399352	83
2	5797.025	34.8113367549521	81.1000000000004
3	5956.825	68.7334646781805	153.1
4	6128.875	82.1944189102886	177.8
5	6450.05	120.4913689855	267
6	6626.525	23.4794058698254	50.6999999999998
7	6547.725	14.3613776033731	33
8	6466.975	21.9343528131864	45.1000000000004
9	6402.95	22.2072810882678	53.3000000000002
10	6238.6	21.3168165227985	48.0999999999995
11	6188.575	38.0485107023478	84.5
12	6132.475	16.5080535900914	36
13	6148.8	29.2958472597513	64.8000000000002
14	6201.175	26.1485977954206	62.6000000000004
15	6278.975	65.2020641288807	148
16	6462.35	71.3056098774843	150
17	6707.325	89.8813430770442	207.7
18	6890.725	33.8618147771203	74.5
19	7115.5	61.534759824563	135.4
20	7209.6	52.8804941983965	102.2
21	7235.05	30.0366997743317	67.5
22	7176.55	27.2397381289419	66
23	7181.45	29.7032545915986	64.5999999999995
24	7230.4	53.2630578669055	123
25	7258.45	50.645072152514	110.900000000001
26	7050.975	50.6503290545416	107.7
27	7022.825	18.1354854727778	43.5
28	6921.9	47.5445755756286	101.599999999999
29	6915.8	31.0494229683367	60.1999999999998

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 5761.425 & 37.3259851399352 & 83 \tabularnewline
2 & 5797.025 & 34.8113367549521 & 81.1000000000004 \tabularnewline
3 & 5956.825 & 68.7334646781805 & 153.1 \tabularnewline
4 & 6128.875 & 82.1944189102886 & 177.8 \tabularnewline
5 & 6450.05 & 120.4913689855 & 267 \tabularnewline
6 & 6626.525 & 23.4794058698254 & 50.6999999999998 \tabularnewline
7 & 6547.725 & 14.3613776033731 & 33 \tabularnewline
8 & 6466.975 & 21.9343528131864 & 45.1000000000004 \tabularnewline
9 & 6402.95 & 22.2072810882678 & 53.3000000000002 \tabularnewline
10 & 6238.6 & 21.3168165227985 & 48.0999999999995 \tabularnewline
11 & 6188.575 & 38.0485107023478 & 84.5 \tabularnewline
12 & 6132.475 & 16.5080535900914 & 36 \tabularnewline
13 & 6148.8 & 29.2958472597513 & 64.8000000000002 \tabularnewline
14 & 6201.175 & 26.1485977954206 & 62.6000000000004 \tabularnewline
15 & 6278.975 & 65.2020641288807 & 148 \tabularnewline
16 & 6462.35 & 71.3056098774843 & 150 \tabularnewline
17 & 6707.325 & 89.8813430770442 & 207.7 \tabularnewline
18 & 6890.725 & 33.8618147771203 & 74.5 \tabularnewline
19 & 7115.5 & 61.534759824563 & 135.4 \tabularnewline
20 & 7209.6 & 52.8804941983965 & 102.2 \tabularnewline
21 & 7235.05 & 30.0366997743317 & 67.5 \tabularnewline
22 & 7176.55 & 27.2397381289419 & 66 \tabularnewline
23 & 7181.45 & 29.7032545915986 & 64.5999999999995 \tabularnewline
24 & 7230.4 & 53.2630578669055 & 123 \tabularnewline
25 & 7258.45 & 50.645072152514 & 110.900000000001 \tabularnewline
26 & 7050.975 & 50.6503290545416 & 107.7 \tabularnewline
27 & 7022.825 & 18.1354854727778 & 43.5 \tabularnewline
28 & 6921.9 & 47.5445755756286 & 101.599999999999 \tabularnewline
29 & 6915.8 & 31.0494229683367 & 60.1999999999998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301809&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]5761.425[/C][C]37.3259851399352[/C][C]83[/C][/ROW]
[ROW][C]2[/C][C]5797.025[/C][C]34.8113367549521[/C][C]81.1000000000004[/C][/ROW]
[ROW][C]3[/C][C]5956.825[/C][C]68.7334646781805[/C][C]153.1[/C][/ROW]
[ROW][C]4[/C][C]6128.875[/C][C]82.1944189102886[/C][C]177.8[/C][/ROW]
[ROW][C]5[/C][C]6450.05[/C][C]120.4913689855[/C][C]267[/C][/ROW]
[ROW][C]6[/C][C]6626.525[/C][C]23.4794058698254[/C][C]50.6999999999998[/C][/ROW]
[ROW][C]7[/C][C]6547.725[/C][C]14.3613776033731[/C][C]33[/C][/ROW]
[ROW][C]8[/C][C]6466.975[/C][C]21.9343528131864[/C][C]45.1000000000004[/C][/ROW]
[ROW][C]9[/C][C]6402.95[/C][C]22.2072810882678[/C][C]53.3000000000002[/C][/ROW]
[ROW][C]10[/C][C]6238.6[/C][C]21.3168165227985[/C][C]48.0999999999995[/C][/ROW]
[ROW][C]11[/C][C]6188.575[/C][C]38.0485107023478[/C][C]84.5[/C][/ROW]
[ROW][C]12[/C][C]6132.475[/C][C]16.5080535900914[/C][C]36[/C][/ROW]
[ROW][C]13[/C][C]6148.8[/C][C]29.2958472597513[/C][C]64.8000000000002[/C][/ROW]
[ROW][C]14[/C][C]6201.175[/C][C]26.1485977954206[/C][C]62.6000000000004[/C][/ROW]
[ROW][C]15[/C][C]6278.975[/C][C]65.2020641288807[/C][C]148[/C][/ROW]
[ROW][C]16[/C][C]6462.35[/C][C]71.3056098774843[/C][C]150[/C][/ROW]
[ROW][C]17[/C][C]6707.325[/C][C]89.8813430770442[/C][C]207.7[/C][/ROW]
[ROW][C]18[/C][C]6890.725[/C][C]33.8618147771203[/C][C]74.5[/C][/ROW]
[ROW][C]19[/C][C]7115.5[/C][C]61.534759824563[/C][C]135.4[/C][/ROW]
[ROW][C]20[/C][C]7209.6[/C][C]52.8804941983965[/C][C]102.2[/C][/ROW]
[ROW][C]21[/C][C]7235.05[/C][C]30.0366997743317[/C][C]67.5[/C][/ROW]
[ROW][C]22[/C][C]7176.55[/C][C]27.2397381289419[/C][C]66[/C][/ROW]
[ROW][C]23[/C][C]7181.45[/C][C]29.7032545915986[/C][C]64.5999999999995[/C][/ROW]
[ROW][C]24[/C][C]7230.4[/C][C]53.2630578669055[/C][C]123[/C][/ROW]
[ROW][C]25[/C][C]7258.45[/C][C]50.645072152514[/C][C]110.900000000001[/C][/ROW]
[ROW][C]26[/C][C]7050.975[/C][C]50.6503290545416[/C][C]107.7[/C][/ROW]
[ROW][C]27[/C][C]7022.825[/C][C]18.1354854727778[/C][C]43.5[/C][/ROW]
[ROW][C]28[/C][C]6921.9[/C][C]47.5445755756286[/C][C]101.599999999999[/C][/ROW]
[ROW][C]29[/C][C]6915.8[/C][C]31.0494229683367[/C][C]60.1999999999998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301809&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301809&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
Section	Mean	Standard Deviation	Range
1	5761.425	37.3259851399352	83
2	5797.025	34.8113367549521	81.1000000000004
3	5956.825	68.7334646781805	153.1
4	6128.875	82.1944189102886	177.8
5	6450.05	120.4913689855	267
6	6626.525	23.4794058698254	50.6999999999998
7	6547.725	14.3613776033731	33
8	6466.975	21.9343528131864	45.1000000000004
9	6402.95	22.2072810882678	53.3000000000002
10	6238.6	21.3168165227985	48.0999999999995
11	6188.575	38.0485107023478	84.5
12	6132.475	16.5080535900914	36
13	6148.8	29.2958472597513	64.8000000000002
14	6201.175	26.1485977954206	62.6000000000004
15	6278.975	65.2020641288807	148
16	6462.35	71.3056098774843	150
17	6707.325	89.8813430770442	207.7
18	6890.725	33.8618147771203	74.5
19	7115.5	61.534759824563	135.4
20	7209.6	52.8804941983965	102.2
21	7235.05	30.0366997743317	67.5
22	7176.55	27.2397381289419	66
23	7181.45	29.7032545915986	64.5999999999995
24	7230.4	53.2630578669055	123
25	7258.45	50.645072152514	110.900000000001
26	7050.975	50.6503290545416	107.7
27	7022.825	18.1354854727778	43.5
28	6921.9	47.5445755756286	101.599999999999
29	6915.8	31.0494229683367	60.1999999999998

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	52.7127196595841
beta	-0.00135039330925542
S.D.	0.010189828258748
T-STAT	-0.132523657412587
p-value	0.895552823669707

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 52.7127196595841 \tabularnewline
beta & -0.00135039330925542 \tabularnewline
S.D. & 0.010189828258748 \tabularnewline
T-STAT & -0.132523657412587 \tabularnewline
p-value & 0.895552823669707 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301809&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]52.7127196595841[/C][/ROW]
[ROW][C]beta[/C][C]-0.00135039330925542[/C][/ROW]
[ROW][C]S.D.[/C][C]0.010189828258748[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.132523657412587[/C][/ROW]
[ROW][C]p-value[/C][C]0.895552823669707[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301809&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301809&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	52.7127196595841
beta	-0.00135039330925542
S.D.	0.010189828258748
T-STAT	-0.132523657412587
p-value	0.895552823669707

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	1.56600289798322
beta	0.2353132096901
S.D.	1.44351876040846
T-STAT	0.163013613777707
p-value	0.871721708268906
Lambda	0.7646867903099

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 1.56600289798322 \tabularnewline
beta & 0.2353132096901 \tabularnewline
S.D. & 1.44351876040846 \tabularnewline
T-STAT & 0.163013613777707 \tabularnewline
p-value & 0.871721708268906 \tabularnewline
Lambda & 0.7646867903099 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301809&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]1.56600289798322[/C][/ROW]
[ROW][C]beta[/C][C]0.2353132096901[/C][/ROW]
[ROW][C]S.D.[/C][C]1.44351876040846[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.163013613777707[/C][/ROW]
[ROW][C]p-value[/C][C]0.871721708268906[/C][/ROW]
[ROW][C]Lambda[/C][C]0.7646867903099[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301809&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301809&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	1.56600289798322
beta	0.2353132096901
S.D.	1.44351876040846
T-STAT	0.163013613777707
p-value	0.871721708268906
Lambda	0.7646867903099

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 12 ; par2 = Triple ; par3 = additive ; par4 = 12 ;

Parameters (R input):

par1 = 4 ;

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code