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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 computationWed, 22 Dec 2010 19:17:33 +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/22/t1293045326n1stqrxvaxglznt.htm/, Retrieved Mon, 06 May 2024 10:41:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114515, Retrieved Mon, 06 May 2024 10:41:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact180

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Variance Reduction Matrix] [Unemployment] [2010-11-29 09:29:57] [b98453cac15ba1066b407e146608df68]
-   PD  [Variance Reduction Matrix] [WS9 - Variance Re...] [2010-12-04 11:04:59] [8ef49741e164ec6343c90c7935194465]
-   P     [Variance Reduction Matrix] [WS 9 VRM] [2010-12-05 14:01:21] [8214fe6d084e5ad7598b249a26cc9f06]
- RMPD      [(Partial) Autocorrelation Function] [paper ACF] [2010-12-10 10:47:04] [8214fe6d084e5ad7598b249a26cc9f06]
-   P         [(Partial) Autocorrelation Function] [paper acf met D=1] [2010-12-10 11:19:24] [8214fe6d084e5ad7598b249a26cc9f06]
- RMP           [Spectral Analysis] [paper - cum perio...] [2010-12-10 11:22:34] [8214fe6d084e5ad7598b249a26cc9f06]
-   P             [Spectral Analysis] [paper - cum perio...] [2010-12-10 11:27:22] [8214fe6d084e5ad7598b249a26cc9f06]
-   PD              [Spectral Analysis] [cum periodogram 2 ] [2010-12-20 20:28:39] [8214fe6d084e5ad7598b249a26cc9f06]
-    D                [Spectral Analysis] [cum per 2 paper] [2010-12-22 13:46:56] [8214fe6d084e5ad7598b249a26cc9f06]
-   PD                  [Spectral Analysis] [cum per 1 middeng...] [2010-12-22 19:06:59] [8214fe6d084e5ad7598b249a26cc9f06]
-   P                     [Spectral Analysis] [cum per 2 middeng...] [2010-12-22 19:08:52] [8214fe6d084e5ad7598b249a26cc9f06]
-   PD                      [Spectral Analysis] [cum per 1 hoogges...] [2010-12-22 19:10:38] [8214fe6d084e5ad7598b249a26cc9f06]
-   P                         [Spectral Analysis] [cum per 2 hoogges...] [2010-12-22 19:12:39] [8214fe6d084e5ad7598b249a26cc9f06]
- RMPD                          [Standard Deviation-Mean Plot] [sdmp laaggeschoolden] [2010-12-22 19:15:16] [8214fe6d084e5ad7598b249a26cc9f06]
-    D                              [Standard Deviation-Mean Plot] [sdmp middengescho...] [2010-12-22 19:17:33] [b47314d83d48c7bf812ec2bcd743b159] [Current]
-    D                                [Standard Deviation-Mean Plot] [sdmp hooggeschoolden] [2010-12-22 19:20:25] [8214fe6d084e5ad7598b249a26cc9f06]
-    D                                [Standard Deviation-Mean Plot] [sdmp hooggeschoolden] [2010-12-22 19:20:25] [8214fe6d084e5ad7598b249a26cc9f06]
- RMPD                                [ARIMA Backward Selection] [arima backward se...] [2010-12-22 19:29:00] [8214fe6d084e5ad7598b249a26cc9f06]
-   PD                                  [ARIMA Backward Selection] [arima backward se...] [2010-12-22 19:34:28] [8214fe6d084e5ad7598b249a26cc9f06]
-    D                                    [ARIMA Backward Selection] [arima backward se...] [2010-12-22 22:09:56] [8214fe6d084e5ad7598b249a26cc9f06]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
56190
54300
51362
49802
48088
46696
56586
64148
56449
52538
49359
49583
51050
49610
48321
47692
46243
46248
56381
62329
60673
58393
55742
57135
57961
56571
55615
53494
52623
52820
66825
70695
65660
63238
61741
63642
65521
64006
62728
62438
61109
63422
78094
82030
75892
72431
69194
71171
72545
71503
69624
67407
66103
67466
81088
86781
79964
80407
76589
78083
78000
76431
75461
73739
71988
72929
85785
89261
84012
80924
76588
77546
73054
73430
71093
72202
70872
70452
80506
80400
77613
69056
65321
64018
64767
61099
58329
56396
54656
55259
66912
66631
59907
56274
54045
55792
55499
53216
52259
51257
48150
51125
61046
61022
56742
54485
53862
58228
61951
62874
64013
62937
61897
65267
75228
76161
71480
69070
68293
74685
72664
71965
69238
67738
65187
66170
77309
77134
70957
67749
65081

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'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 & 1 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114515&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114515&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114515&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
152925.08333333334887.4261018981317452
253318.08333333335771.6588360450616086
360073.756053.9860816880618072
4690036936.9052570620120921
574796.66666666676619.4970329849820678
678555.33333333335389.6615905459317273
772334.755202.7053473955416488
859172.254683.9517334094212867
954740.91666666673976.3029758886112896
1067821.33333333335440.63441042314264

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 52925.0833333333 & 4887.42610189813 & 17452 \tabularnewline
2 & 53318.0833333333 & 5771.65883604506 & 16086 \tabularnewline
3 & 60073.75 & 6053.98608168806 & 18072 \tabularnewline
4 & 69003 & 6936.90525706201 & 20921 \tabularnewline
5 & 74796.6666666667 & 6619.49703298498 & 20678 \tabularnewline
6 & 78555.3333333333 & 5389.66159054593 & 17273 \tabularnewline
7 & 72334.75 & 5202.70534739554 & 16488 \tabularnewline
8 & 59172.25 & 4683.95173340942 & 12867 \tabularnewline
9 & 54740.9166666667 & 3976.30297588861 & 12896 \tabularnewline
10 & 67821.3333333333 & 5440.634410423 & 14264 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114515&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]52925.0833333333[/C][C]4887.42610189813[/C][C]17452[/C][/ROW]
[ROW][C]2[/C][C]53318.0833333333[/C][C]5771.65883604506[/C][C]16086[/C][/ROW]
[ROW][C]3[/C][C]60073.75[/C][C]6053.98608168806[/C][C]18072[/C][/ROW]
[ROW][C]4[/C][C]69003[/C][C]6936.90525706201[/C][C]20921[/C][/ROW]
[ROW][C]5[/C][C]74796.6666666667[/C][C]6619.49703298498[/C][C]20678[/C][/ROW]
[ROW][C]6[/C][C]78555.3333333333[/C][C]5389.66159054593[/C][C]17273[/C][/ROW]
[ROW][C]7[/C][C]72334.75[/C][C]5202.70534739554[/C][C]16488[/C][/ROW]
[ROW][C]8[/C][C]59172.25[/C][C]4683.95173340942[/C][C]12867[/C][/ROW]
[ROW][C]9[/C][C]54740.9166666667[/C][C]3976.30297588861[/C][C]12896[/C][/ROW]
[ROW][C]10[/C][C]67821.3333333333[/C][C]5440.634410423[/C][C]14264[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114515&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114515&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
152925.08333333334887.4261018981317452
253318.08333333335771.6588360450616086
360073.756053.9860816880618072
4690036936.9052570620120921
574796.66666666676619.4970329849820678
678555.33333333335389.6615905459317273
772334.755202.7053473955416488
859172.254683.9517334094212867
954740.91666666673976.3029758886112896
1067821.33333333335440.63441042314264






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha2698.91699707613
beta0.0435222774692551
S.D.0.0297599434898936
T-STAT1.46244489624233
p-value0.181763366413417

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 2698.91699707613 \tabularnewline
beta & 0.0435222774692551 \tabularnewline
S.D. & 0.0297599434898936 \tabularnewline
T-STAT & 1.46244489624233 \tabularnewline
p-value & 0.181763366413417 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114515&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]2698.91699707613[/C][/ROW]
[ROW][C]beta[/C][C]0.0435222774692551[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0297599434898936[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.46244489624233[/C][/ROW]
[ROW][C]p-value[/C][C]0.181763366413417[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114515&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114515&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)
alpha2698.91699707613
beta0.0435222774692551
S.D.0.0297599434898936
T-STAT1.46244489624233
p-value0.181763366413417






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha2.6188757205193
beta0.540700186138982
S.D.0.349800108932904
T-STAT1.54574047386216
p-value0.160750437964045
Lambda0.459299813861018

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 2.6188757205193 \tabularnewline
beta & 0.540700186138982 \tabularnewline
S.D. & 0.349800108932904 \tabularnewline
T-STAT & 1.54574047386216 \tabularnewline
p-value & 0.160750437964045 \tabularnewline
Lambda & 0.459299813861018 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114515&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]2.6188757205193[/C][/ROW]
[ROW][C]beta[/C][C]0.540700186138982[/C][/ROW]
[ROW][C]S.D.[/C][C]0.349800108932904[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.54574047386216[/C][/ROW]
[ROW][C]p-value[/C][C]0.160750437964045[/C][/ROW]
[ROW][C]Lambda[/C][C]0.459299813861018[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114515&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114515&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)
alpha2.6188757205193
beta0.540700186138982
S.D.0.349800108932904
T-STAT1.54574047386216
p-value0.160750437964045
Lambda0.459299813861018


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')