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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_centraltendency.wasp

Title produced by software

Central Tendency

Date of computation

Wed, 30 Jan 2019 22:21:36 +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/2019/Jan/30/t1548883320sg5ehqgndsgkrks.htm/, Retrieved Sat, 27 Apr 2024 16:53:20 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=317076, Retrieved Sat, 27 Apr 2024 16:53:20 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

115

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

-       [Central Tendency] [] [2019-01-30 21:21:36] [4b9727532e3f43930b93a8759cb851b1] [Current]

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Dataseries X:

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Histogram

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0.7923
-2.468
-2.996
3.119
0.04315
0.731
2.45
2.119
-1.429
-1.644
-3.065
-1.461
1.141
1.329
0.3396
0.8429
2.225
-1.924
0.4999
-0.6433

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 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 time2 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317076&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]

2 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=317076&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317076&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	2 seconds
R Server	Big Analytics Cloud Computing Center

Central Tendency - Ungrouped Data
Measure	Value	S.E.	Value/S.E.
Arithmetic Mean	7.75e-05	0.41655	0.000186052
Geometric Mean	NaN
Harmonic Mean	0.664815
Quadratic Mean	1.8157
Winsorized Mean ( 1 / 6 )	-0.0299225	0.40319	-0.0742144
Winsorized Mean ( 2 / 6 )	0.0003775	0.376682	0.00100217
Winsorized Mean ( 3 / 6 )	0.0660775	0.345198	0.191419
Winsorized Mean ( 4 / 6 )	-0.0359225	0.28308	-0.126899
Winsorized Mean ( 5 / 6 )	-0.0371725	0.257959	-0.144102
Winsorized Mean ( 6 / 6 )	-0.117003	0.234728	-0.498461
Trimmed Mean ( 1 / 6 )	-0.00291389	0.391131	-0.00744991
Trimmed Mean ( 2 / 6 )	0.0308469	0.364116	0.0847172
Trimmed Mean ( 3 / 6 )	0.0526107	0.337689	0.155797
Trimmed Mean ( 4 / 6 )	0.0451292	0.308669	0.146206
Trimmed Mean ( 5 / 6 )	0.085655	0.299232	0.286249
Trimmed Mean ( 6 / 6 )	0.147069	0.284182	0.517517
Median	0.41975
Midrange	0.027
Midmean - Weighted Average at Xnp	-0.0715864
Midmean - Weighted Average at X(n+1)p	0.085655
Midmean - Empirical Distribution Function	-0.0715864
Midmean - Empirical Distribution Function - Averaging	0.085655
Midmean - Empirical Distribution Function - Interpolation	0.085655
Midmean - Closest Observation	-0.0715864
Midmean - True Basic - Statistics Graphics Toolkit	0.085655
Midmean - MS Excel (old versions)	0.0451292
Number of observations	20

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 7.75e-05 & 0.41655 & 0.000186052 \tabularnewline
Geometric Mean & NaN &  &  \tabularnewline
Harmonic Mean & 0.664815 &  &  \tabularnewline
Quadratic Mean & 1.8157 &  &  \tabularnewline
Winsorized Mean ( 1 / 6 ) & -0.0299225 & 0.40319 & -0.0742144 \tabularnewline
Winsorized Mean ( 2 / 6 ) & 0.0003775 & 0.376682 & 0.00100217 \tabularnewline
Winsorized Mean ( 3 / 6 ) & 0.0660775 & 0.345198 & 0.191419 \tabularnewline
Winsorized Mean ( 4 / 6 ) & -0.0359225 & 0.28308 & -0.126899 \tabularnewline
Winsorized Mean ( 5 / 6 ) & -0.0371725 & 0.257959 & -0.144102 \tabularnewline
Winsorized Mean ( 6 / 6 ) & -0.117003 & 0.234728 & -0.498461 \tabularnewline
Trimmed Mean ( 1 / 6 ) & -0.00291389 & 0.391131 & -0.00744991 \tabularnewline
Trimmed Mean ( 2 / 6 ) & 0.0308469 & 0.364116 & 0.0847172 \tabularnewline
Trimmed Mean ( 3 / 6 ) & 0.0526107 & 0.337689 & 0.155797 \tabularnewline
Trimmed Mean ( 4 / 6 ) & 0.0451292 & 0.308669 & 0.146206 \tabularnewline
Trimmed Mean ( 5 / 6 ) & 0.085655 & 0.299232 & 0.286249 \tabularnewline
Trimmed Mean ( 6 / 6 ) & 0.147069 & 0.284182 & 0.517517 \tabularnewline
Median & 0.41975 &  &  \tabularnewline
Midrange & 0.027 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & -0.0715864 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 0.085655 &  &  \tabularnewline
Midmean - Empirical Distribution Function & -0.0715864 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 0.085655 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 0.085655 &  &  \tabularnewline
Midmean - Closest Observation & -0.0715864 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 0.085655 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 0.0451292 &  &  \tabularnewline
Number of observations & 20 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317076&T=1

[TABLE]
[ROW][C]Central Tendency - Ungrouped Data[/C][/ROW]
[ROW][C]Measure[/C][C]Value[/C][C]S.E.[/C][C]Value/S.E.[/C][/ROW]
[ROW][C]Arithmetic Mean[/C][C]7.75e-05[/C][C]0.41655[/C][C]0.000186052[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]NaN[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]0.664815[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]1.8157[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 6 )[/C][C]-0.0299225[/C][C]0.40319[/C][C]-0.0742144[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 6 )[/C][C]0.0003775[/C][C]0.376682[/C][C]0.00100217[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 6 )[/C][C]0.0660775[/C][C]0.345198[/C][C]0.191419[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 6 )[/C][C]-0.0359225[/C][C]0.28308[/C][C]-0.126899[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 6 )[/C][C]-0.0371725[/C][C]0.257959[/C][C]-0.144102[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 6 )[/C][C]-0.117003[/C][C]0.234728[/C][C]-0.498461[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 6 )[/C][C]-0.00291389[/C][C]0.391131[/C][C]-0.00744991[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 6 )[/C][C]0.0308469[/C][C]0.364116[/C][C]0.0847172[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 6 )[/C][C]0.0526107[/C][C]0.337689[/C][C]0.155797[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 6 )[/C][C]0.0451292[/C][C]0.308669[/C][C]0.146206[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 6 )[/C][C]0.085655[/C][C]0.299232[/C][C]0.286249[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 6 )[/C][C]0.147069[/C][C]0.284182[/C][C]0.517517[/C][/ROW]
[ROW][C]Median[/C][C]0.41975[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]0.027[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]-0.0715864[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]0.085655[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]-0.0715864[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]0.085655[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]0.085655[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]-0.0715864[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]0.085655[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]0.0451292[/C][C][/C][C][/C][/ROW]
[ROW][C]Number of observations[/C][C]20[/C][C][/C][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317076&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317076&T=1

As an alternative you can also use a QR Code:

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

Central Tendency - Ungrouped Data
Measure	Value	S.E.	Value/S.E.
Arithmetic Mean	7.75e-05	0.41655	0.000186052
Geometric Mean	NaN
Harmonic Mean	0.664815
Quadratic Mean	1.8157
Winsorized Mean ( 1 / 6 )	-0.0299225	0.40319	-0.0742144
Winsorized Mean ( 2 / 6 )	0.0003775	0.376682	0.00100217
Winsorized Mean ( 3 / 6 )	0.0660775	0.345198	0.191419
Winsorized Mean ( 4 / 6 )	-0.0359225	0.28308	-0.126899
Winsorized Mean ( 5 / 6 )	-0.0371725	0.257959	-0.144102
Winsorized Mean ( 6 / 6 )	-0.117003	0.234728	-0.498461
Trimmed Mean ( 1 / 6 )	-0.00291389	0.391131	-0.00744991
Trimmed Mean ( 2 / 6 )	0.0308469	0.364116	0.0847172
Trimmed Mean ( 3 / 6 )	0.0526107	0.337689	0.155797
Trimmed Mean ( 4 / 6 )	0.0451292	0.308669	0.146206
Trimmed Mean ( 5 / 6 )	0.085655	0.299232	0.286249
Trimmed Mean ( 6 / 6 )	0.147069	0.284182	0.517517
Median	0.41975
Midrange	0.027
Midmean - Weighted Average at Xnp	-0.0715864
Midmean - Weighted Average at X(n+1)p	0.085655
Midmean - Empirical Distribution Function	-0.0715864
Midmean - Empirical Distribution Function - Averaging	0.085655
Midmean - Empirical Distribution Function - Interpolation	0.085655
Midmean - Closest Observation	-0.0715864
Midmean - True Basic - Statistics Graphics Toolkit	0.085655
Midmean - MS Excel (old versions)	0.0451292
Number of observations	20

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

Parameters (R input):

R code (references can be found in the software module):

geomean <- function(x) {
return(exp(mean(log(x))))
}
harmean <- function(x) {
return(1/mean(1/x))
}
quamean <- function(x) {
return(sqrt(mean(x*x)))
}
winmean <- function(x) {
x <-sort(x[!is.na(x)])
n<-length(x)
denom <- 3
nodenom <- n/denom
if (nodenom>40) denom <- n/40
sqrtn = sqrt(n)
roundnodenom = floor(nodenom)
win <- array(NA,dim=c(roundnodenom,2))
for (j in 1:roundnodenom) {
win[j,1] <- (j*x[j+1]+sum(x[(j+1):(n-j)])+j*x[n-j])/n
win[j,2] <- sd(c(rep(x[j+1],j),x[(j+1):(n-j)],rep(x[n-j],j)))/sqrtn
}
return(win)
}
trimean <- function(x) {
x <-sort(x[!is.na(x)])
n<-length(x)
denom <- 3
nodenom <- n/denom
if (nodenom>40) denom <- n/40
sqrtn = sqrt(n)
roundnodenom = floor(nodenom)
tri <- array(NA,dim=c(roundnodenom,2))
for (j in 1:roundnodenom) {
tri[j,1] <- mean(x,trim=j/n)
tri[j,2] <- sd(x[(j+1):(n-j)]) / sqrt(n-j*2)
}
return(tri)
}
midrange <- function(x) {
return((max(x)+min(x))/2)
}
q1 <- function(data,n,p,i,f) {
np <- n*p;
i <<- floor(np)
f <<- np - i
qvalue <- (1-f)*data[i] + f*data[i+1]
}
q2 <- function(data,n,p,i,f) {
np <- (n+1)*p
i <<- floor(np)
f <<- np - i
qvalue <- (1-f)*data[i] + f*data[i+1]
}
q3 <- function(data,n,p,i,f) {
np <- n*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- data[i]
} else {
qvalue <- data[i+1]
}
}
q4 <- function(data,n,p,i,f) {
np <- n*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- (data[i]+data[i+1])/2
} else {
qvalue <- data[i+1]
}
}
q5 <- function(data,n,p,i,f) {
np <- (n-1)*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- data[i+1]
} else {
qvalue <- data[i+1] + f*(data[i+2]-data[i+1])
}
}
q6 <- function(data,n,p,i,f) {
np <- n*p+0.5
i <<- floor(np)
f <<- np - i
qvalue <- data[i]
}
q7 <- function(data,n,p,i,f) {
np <- (n+1)*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- data[i]
} else {
qvalue <- f*data[i] + (1-f)*data[i+1]
}
}
q8 <- function(data,n,p,i,f) {
np <- (n+1)*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- data[i]
} else {
if (f == 0.5) {
qvalue <- (data[i]+data[i+1])/2
} else {
if (f < 0.5) {
qvalue <- data[i]
} else {
qvalue <- data[i+1]
}
}
}
}
midmean <- function(x,def) {
x <-sort(x[!is.na(x)])
n<-length(x)
if (def==1) {
qvalue1 <- q1(x,n,0.25,i,f)
qvalue3 <- q1(x,n,0.75,i,f)
}
if (def==2) {
qvalue1 <- q2(x,n,0.25,i,f)
qvalue3 <- q2(x,n,0.75,i,f)
}
if (def==3) {
qvalue1 <- q3(x,n,0.25,i,f)
qvalue3 <- q3(x,n,0.75,i,f)
}
if (def==4) {
qvalue1 <- q4(x,n,0.25,i,f)
qvalue3 <- q4(x,n,0.75,i,f)
}
if (def==5) {
qvalue1 <- q5(x,n,0.25,i,f)
qvalue3 <- q5(x,n,0.75,i,f)
}
if (def==6) {
qvalue1 <- q6(x,n,0.25,i,f)
qvalue3 <- q6(x,n,0.75,i,f)
}
if (def==7) {
qvalue1 <- q7(x,n,0.25,i,f)
qvalue3 <- q7(x,n,0.75,i,f)
}
if (def==8) {
qvalue1 <- q8(x,n,0.25,i,f)
qvalue3 <- q8(x,n,0.75,i,f)
}
midm <- 0
myn <- 0
roundno4 <- round(n/4)
round3no4 <- round(3*n/4)
for (i in 1:n) {
if ((x[i]>=qvalue1) & (x[i]<=qvalue3)){
midm = midm + x[i]
myn = myn + 1
}
}
midm = midm / myn
return(midm)
}
(arm <- mean(x))
sqrtn <- sqrt(length(x))
(armse <- sd(x) / sqrtn)
(armose <- arm / armse)
(geo <- geomean(x))
(har <- harmean(x))
(qua <- quamean(x))
(win <- winmean(x))
(tri <- trimean(x))
(midr <- midrange(x))
midm <- array(NA,dim=8)
for (j in 1:8) midm[j] <- midmean(x,j)
midm
bitmap(file='test1.png')
lb <- win[,1] - 2*win[,2]
ub <- win[,1] + 2*win[,2]
if ((ylimmin == '') | (ylimmax == '')) plot(win[,1],type='b',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(win[,1],type='l',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(ylimmin,ylimmax))
lines(ub,lty=3)
lines(lb,lty=3)
grid()
dev.off()
bitmap(file='test2.png')
lb <- tri[,1] - 2*tri[,2]
ub <- tri[,1] + 2*tri[,2]
if ((ylimmin == '') | (ylimmax == '')) plot(tri[,1],type='b',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(tri[,1],type='l',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(ylimmin,ylimmax))
lines(ub,lty=3)
lines(lb,lty=3)
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Central Tendency - Ungrouped Data',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Measure',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.element(a,'S.E.',header=TRUE)
a<-table.element(a,'Value/S.E.',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Arithmetic Mean',header=TRUE)
a<-table.element(a,signif(arm,6))
a<-table.element(a, signif(armse,6))
a<-table.element(a,signif(armose,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Geometric Mean',header=TRUE)
a<-table.element(a,signif(geo,6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Harmonic Mean',header=TRUE)
a<-table.element(a,signif(har,6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Quadratic Mean',header=TRUE)
a<-table.element(a,signif(qua,6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
for (j in 1:length(win[,1])) {
a<-table.row.start(a)
mylabel <- paste('Winsorized Mean (',j)
mylabel <- paste(mylabel,'/')
mylabel <- paste(mylabel,length(win[,1]))
mylabel <- paste(mylabel,')')
a<-table.element(a, mylabel,header=TRUE)
a<-table.element(a,signif(win[j,1],6))
a<-table.element(a,signif(win[j,2],6))
a<-table.element(a,signif(win[j,1]/win[j,2],6))
a<-table.row.end(a)
}
for (j in 1:length(tri[,1])) {
a<-table.row.start(a)
mylabel <- paste('Trimmed Mean (',j)
mylabel <- paste(mylabel,'/')
mylabel <- paste(mylabel,length(tri[,1]))
mylabel <- paste(mylabel,')')
a<-table.element(a, mylabel,header=TRUE)
a<-table.element(a,signif(tri[j,1],6))
a<-table.element(a,signif(tri[j,2],6))
a<-table.element(a,signif(tri[j,1]/tri[j,2],6))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a, 'Median',header=TRUE)
a<-table.element(a,signif(median(x),6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Midrange',header=TRUE)
a<-table.element(a,signif(midr,6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <-  'Midmean'
mylabel <- paste(mymid,'Weighted Average at Xnp',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[1],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <-  'Midmean'
mylabel <- paste(mymid,'Weighted Average at X(n+1)p',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[2],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <-  'Midmean'
mylabel <- paste(mymid,'Empirical Distribution Function',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[3],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <-  'Midmean'
mylabel <- paste(mymid,'Empirical Distribution Function - Averaging',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[4],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <-  'Midmean'
mylabel <- paste(mymid,'Empirical Distribution Function - Interpolation',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[5],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <-  'Midmean'
mylabel <- paste(mymid,'Closest Observation',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[6],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <-  'Midmean'
mylabel <- paste(mymid,'True Basic - Statistics Graphics Toolkit',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[7],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <-  'Midmean'
mylabel <- paste(mymid,'MS Excel (old versions)',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[8],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Number of observations',header=TRUE)
a<-table.element(a,signif(length(x),6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code