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R Software Module

rwasp_smp.wasp

Title produced by software

Standard Deviation-Mean Plot

Date of computation

Mon, 08 Dec 2008 11:58:57 -0700

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/2008/Dec/08/t1228762953bq7w4dbcwx6q42v.htm/, Retrieved Thu, 16 May 2024 21:08:46 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=30728, Retrieved Thu, 16 May 2024 21:08:46 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

215

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] [SMP of Unemployme...] [2008-12-08 18:58:57] [02e7fb326979b65614900650d62c19a6] [Current]

Feedback Forum

2008-12-12 12:35:17 [Liese Tormans] [reply] 
De student begrijpt het principe toch wil ik hier en daar nog enkele correcties aanbrengen. 
De outliers hebben enkel invloed op de regressierechte wanneer ze zich links of rechts in de scatterplot bevinden. Dus niet zoals de student vermeld heeft in het begin of op het einde van de reeks. We moeten dus enkel rekening houden met de outliers aan de linker of rechter kant van de reeks, en enkel deze moeten eventueel geëlimineerd worden. 
 
Ook in de tweede paragraaf heeft de student een kleine fout gemaakt omtrent de conclusie. De p-waarde  van 0,03 heeft  betrekking op de beta-coefficient (= de helling van de scatterplot) en niet op de standaardfout zoals de student vermeld heeft.  
 
We kunnen hieruit dus concluderen dat de beta coefficient significant verschillend is. Er bestaat dus een niet toevallig verband tussen de standaardfout en het gemiddelde. 
 
In de derde paragraaf klopt de conclusie van de student. 
2008-12-12 12:35:40 [Liese Tormans] [reply] 
De student begrijpt het principe toch wil ik hier en daar nog enkele correcties aanbrengen. 
De outliers hebben enkel invloed op de regressierechte wanneer ze zich links of rechts in de scatterplot bevinden. Dus niet zoals de student vermeld heeft in het begin of op het einde van de reeks. We moeten dus enkel rekening houden met de outliers aan de linker of rechter kant van de reeks, en enkel deze moeten eventueel geëlimineerd worden. 
 
Ook in de tweede paragraaf heeft de student een kleine fout gemaakt omtrent de conclusie. De p-waarde  van 0,03 heeft  betrekking op de beta-coefficient (= de helling van de scatterplot) en niet op de standaardfout zoals de student vermeld heeft.  
 
We kunnen hieruit dus concluderen dat de beta coefficient significant verschillend is. Er bestaat dus een niet toevallig verband tussen de standaardfout en het gemiddelde. 
 
In de derde paragraaf klopt de conclusie van de student. 
 
We moeten dus enkel naar de tweede tabel gaan kijken wanneer er voldaan is aan volgende 2 voorwaarden.  
 
De beta- coefficient van de eerste tabel is significant verschillend 
In de scatterplot zijn geen outliers aanwezig die de helling beïnvloeden 
 
We zien dat in dit geval voldaan is aan deze twee voorwaarden. Op tabel 2 kunnen we dan een Lambda aflezen van 0.46. Zoals de student vermeld heeft in zijn conclusie, gaan we deze waarde gebruiken als optimale transformation parameter om een reeks stationair te maken.  
2008-12-14 13:59:59 [Roland Feldman] [reply] 
De outliers hebben enkel invloed indien ze zich links of rechts van de regressierechte bevinden. De betacoefficient is hier significant verschillend. Men moet de p waarde analyseren ten opzichte van de betacoefficient. We kunnen hier de transformatie dus doorvoeren. 
  
2008-12-15 20:44:42 [Evelien Blockx] [reply] 
De p-value bedraagt 0.00382792717820021. Dat is een p-waarde die kleiner dan 5%. Beta verschilt significant van nul. De p-waarde heeft dus, zoals reeds eerder vermeld in de feedbacks, betrekking op beta. 
 
De gegeven Lambda-waarde kan gebruikt worden als optimale transformatie parameter en kan afgerond worden naar 0,5. 

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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	'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=30728&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=30728&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30728&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	'George Udny Yule' @ 72.249.76.132

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	227.733333333333	29.1940010442162	106
2	363.65	36.3267119348834	139.3
3	328.916666666667	93.5458451857438	273.6
4	205.45	30.3247123946491	89.5
5	188.333333333333	27.3773739803621	86
6	181.25	26.1571995999016	85.2
7	353.341666666667	36.2089506346236	110.8
8	285.308333333333	46.6051783766048	138.5
9	275.125	35.0509272345255	108.8
10	285.85	30.7409262296136	86.7
11	460.166666666667	56.0969831198748	147.3
12	373.95	53.1021228817215	150.3
13	385.1	35.1442999387072	115.5
14	471.358333333333	64.2130184808676	179.1
15	394.208333333333	40.6847628018454	138.7
16	407	46.6030432092544	147.6
17	378.575	49.9696839912143	132
18	336.633333333333	51.5290973993129	146.3
19	287.841666666667	33.2699826033054	112.7
20	297.566666666667	30.476438987082	117
21	281.666666666667	40.7140434412173	131.1
22	283.033333333333	28.4860837901065	109
23	408.85	48.934882520271	128.5
24	499.333333333333	37.5159925494408	109.6
25	484.008333333333	48.4390236808249	133.1
26	430.433333333333	37.4665022103584	108.4
27	507.583333333333	53.8722703730919	196.2
28	782.975	48.4555489832973	137.4
29	728.8	53.3077684940777	187
30	685.558333333333	72.5442618285032	222.9
31	604.716666666667	50.4040372360882	144

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 227.733333333333 & 29.1940010442162 & 106 \tabularnewline
2 & 363.65 & 36.3267119348834 & 139.3 \tabularnewline
3 & 328.916666666667 & 93.5458451857438 & 273.6 \tabularnewline
4 & 205.45 & 30.3247123946491 & 89.5 \tabularnewline
5 & 188.333333333333 & 27.3773739803621 & 86 \tabularnewline
6 & 181.25 & 26.1571995999016 & 85.2 \tabularnewline
7 & 353.341666666667 & 36.2089506346236 & 110.8 \tabularnewline
8 & 285.308333333333 & 46.6051783766048 & 138.5 \tabularnewline
9 & 275.125 & 35.0509272345255 & 108.8 \tabularnewline
10 & 285.85 & 30.7409262296136 & 86.7 \tabularnewline
11 & 460.166666666667 & 56.0969831198748 & 147.3 \tabularnewline
12 & 373.95 & 53.1021228817215 & 150.3 \tabularnewline
13 & 385.1 & 35.1442999387072 & 115.5 \tabularnewline
14 & 471.358333333333 & 64.2130184808676 & 179.1 \tabularnewline
15 & 394.208333333333 & 40.6847628018454 & 138.7 \tabularnewline
16 & 407 & 46.6030432092544 & 147.6 \tabularnewline
17 & 378.575 & 49.9696839912143 & 132 \tabularnewline
18 & 336.633333333333 & 51.5290973993129 & 146.3 \tabularnewline
19 & 287.841666666667 & 33.2699826033054 & 112.7 \tabularnewline
20 & 297.566666666667 & 30.476438987082 & 117 \tabularnewline
21 & 281.666666666667 & 40.7140434412173 & 131.1 \tabularnewline
22 & 283.033333333333 & 28.4860837901065 & 109 \tabularnewline
23 & 408.85 & 48.934882520271 & 128.5 \tabularnewline
24 & 499.333333333333 & 37.5159925494408 & 109.6 \tabularnewline
25 & 484.008333333333 & 48.4390236808249 & 133.1 \tabularnewline
26 & 430.433333333333 & 37.4665022103584 & 108.4 \tabularnewline
27 & 507.583333333333 & 53.8722703730919 & 196.2 \tabularnewline
28 & 782.975 & 48.4555489832973 & 137.4 \tabularnewline
29 & 728.8 & 53.3077684940777 & 187 \tabularnewline
30 & 685.558333333333 & 72.5442618285032 & 222.9 \tabularnewline
31 & 604.716666666667 & 50.4040372360882 & 144 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30728&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]227.733333333333[/C][C]29.1940010442162[/C][C]106[/C][/ROW]
[ROW][C]2[/C][C]363.65[/C][C]36.3267119348834[/C][C]139.3[/C][/ROW]
[ROW][C]3[/C][C]328.916666666667[/C][C]93.5458451857438[/C][C]273.6[/C][/ROW]
[ROW][C]4[/C][C]205.45[/C][C]30.3247123946491[/C][C]89.5[/C][/ROW]
[ROW][C]5[/C][C]188.333333333333[/C][C]27.3773739803621[/C][C]86[/C][/ROW]
[ROW][C]6[/C][C]181.25[/C][C]26.1571995999016[/C][C]85.2[/C][/ROW]
[ROW][C]7[/C][C]353.341666666667[/C][C]36.2089506346236[/C][C]110.8[/C][/ROW]
[ROW][C]8[/C][C]285.308333333333[/C][C]46.6051783766048[/C][C]138.5[/C][/ROW]
[ROW][C]9[/C][C]275.125[/C][C]35.0509272345255[/C][C]108.8[/C][/ROW]
[ROW][C]10[/C][C]285.85[/C][C]30.7409262296136[/C][C]86.7[/C][/ROW]
[ROW][C]11[/C][C]460.166666666667[/C][C]56.0969831198748[/C][C]147.3[/C][/ROW]
[ROW][C]12[/C][C]373.95[/C][C]53.1021228817215[/C][C]150.3[/C][/ROW]
[ROW][C]13[/C][C]385.1[/C][C]35.1442999387072[/C][C]115.5[/C][/ROW]
[ROW][C]14[/C][C]471.358333333333[/C][C]64.2130184808676[/C][C]179.1[/C][/ROW]
[ROW][C]15[/C][C]394.208333333333[/C][C]40.6847628018454[/C][C]138.7[/C][/ROW]
[ROW][C]16[/C][C]407[/C][C]46.6030432092544[/C][C]147.6[/C][/ROW]
[ROW][C]17[/C][C]378.575[/C][C]49.9696839912143[/C][C]132[/C][/ROW]
[ROW][C]18[/C][C]336.633333333333[/C][C]51.5290973993129[/C][C]146.3[/C][/ROW]
[ROW][C]19[/C][C]287.841666666667[/C][C]33.2699826033054[/C][C]112.7[/C][/ROW]
[ROW][C]20[/C][C]297.566666666667[/C][C]30.476438987082[/C][C]117[/C][/ROW]
[ROW][C]21[/C][C]281.666666666667[/C][C]40.7140434412173[/C][C]131.1[/C][/ROW]
[ROW][C]22[/C][C]283.033333333333[/C][C]28.4860837901065[/C][C]109[/C][/ROW]
[ROW][C]23[/C][C]408.85[/C][C]48.934882520271[/C][C]128.5[/C][/ROW]
[ROW][C]24[/C][C]499.333333333333[/C][C]37.5159925494408[/C][C]109.6[/C][/ROW]
[ROW][C]25[/C][C]484.008333333333[/C][C]48.4390236808249[/C][C]133.1[/C][/ROW]
[ROW][C]26[/C][C]430.433333333333[/C][C]37.4665022103584[/C][C]108.4[/C][/ROW]
[ROW][C]27[/C][C]507.583333333333[/C][C]53.8722703730919[/C][C]196.2[/C][/ROW]
[ROW][C]28[/C][C]782.975[/C][C]48.4555489832973[/C][C]137.4[/C][/ROW]
[ROW][C]29[/C][C]728.8[/C][C]53.3077684940777[/C][C]187[/C][/ROW]
[ROW][C]30[/C][C]685.558333333333[/C][C]72.5442618285032[/C][C]222.9[/C][/ROW]
[ROW][C]31[/C][C]604.716666666667[/C][C]50.4040372360882[/C][C]144[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30728&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30728&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	227.733333333333	29.1940010442162	106
2	363.65	36.3267119348834	139.3
3	328.916666666667	93.5458451857438	273.6
4	205.45	30.3247123946491	89.5
5	188.333333333333	27.3773739803621	86
6	181.25	26.1571995999016	85.2
7	353.341666666667	36.2089506346236	110.8
8	285.308333333333	46.6051783766048	138.5
9	275.125	35.0509272345255	108.8
10	285.85	30.7409262296136	86.7
11	460.166666666667	56.0969831198748	147.3
12	373.95	53.1021228817215	150.3
13	385.1	35.1442999387072	115.5
14	471.358333333333	64.2130184808676	179.1
15	394.208333333333	40.6847628018454	138.7
16	407	46.6030432092544	147.6
17	378.575	49.9696839912143	132
18	336.633333333333	51.5290973993129	146.3
19	287.841666666667	33.2699826033054	112.7
20	297.566666666667	30.476438987082	117
21	281.666666666667	40.7140434412173	131.1
22	283.033333333333	28.4860837901065	109
23	408.85	48.934882520271	128.5
24	499.333333333333	37.5159925494408	109.6
25	484.008333333333	48.4390236808249	133.1
26	430.433333333333	37.4665022103584	108.4
27	507.583333333333	53.8722703730919	196.2
28	782.975	48.4555489832973	137.4
29	728.8	53.3077684940777	187
30	685.558333333333	72.5442618285032	222.9
31	604.716666666667	50.4040372360882	144

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	25.0818554501784
beta	0.0488516650103526
S.D.	0.0155384837695400
T-STAT	3.14391453728042
p-value	0.00382792717820021

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 25.0818554501784 \tabularnewline
beta & 0.0488516650103526 \tabularnewline
S.D. & 0.0155384837695400 \tabularnewline
T-STAT & 3.14391453728042 \tabularnewline
p-value & 0.00382792717820021 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30728&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]25.0818554501784[/C][/ROW]
[ROW][C]beta[/C][C]0.0488516650103526[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0155384837695400[/C][/ROW]
[ROW][C]T-STAT[/C][C]3.14391453728042[/C][/ROW]
[ROW][C]p-value[/C][C]0.00382792717820021[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30728&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30728&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	25.0818554501784
beta	0.0488516650103526
S.D.	0.0155384837695400
T-STAT	3.14391453728042
p-value	0.00382792717820021

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	0.596066412617842
beta	0.532942026074986
S.D.	0.115396834084912
T-STAT	4.61834183148243
p-value	7.31833172336408e-05
Lambda	0.467057973925014

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 0.596066412617842 \tabularnewline
beta & 0.532942026074986 \tabularnewline
S.D. & 0.115396834084912 \tabularnewline
T-STAT & 4.61834183148243 \tabularnewline
p-value & 7.31833172336408e-05 \tabularnewline
Lambda & 0.467057973925014 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30728&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.596066412617842[/C][/ROW]
[ROW][C]beta[/C][C]0.532942026074986[/C][/ROW]
[ROW][C]S.D.[/C][C]0.115396834084912[/C][/ROW]
[ROW][C]T-STAT[/C][C]4.61834183148243[/C][/ROW]
[ROW][C]p-value[/C][C]7.31833172336408e-05[/C][/ROW]
[ROW][C]Lambda[/C][C]0.467057973925014[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30728&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30728&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	0.596066412617842
beta	0.532942026074986
S.D.	0.115396834084912
T-STAT	4.61834183148243
p-value	7.31833172336408e-05
Lambda	0.467057973925014

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code