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Author

*The author of this computation has been verified*

R Software Module

rwasp_centraltendency.wasp

Title produced by software

Central Tendency

Date of computation

Thu, 31 Jan 2019 16:15:50 +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/31/t15489478456yv04tgcwat5q0u.htm/, Retrieved Sun, 05 May 2024 08:51:40 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=318458, Retrieved Sun, 05 May 2024 08:51:40 +0000

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Original text written by user:

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Estimated Impact

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

-       [Central Tendency] [] [2019-01-31 15:15:50] [7c8c434c046871cc4d318aab90f50552] [Current]

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-0.628162412817692
-2.46301447929447
-3.75038661297768
0.801641810798906
1.31725030274473
6.07651176840064
6.94589828224875
-0.198358189201074
-2.73650956748366
-0.152997113685818
-3.20400612400917
-1.34586197071795
-1.33516693443564
-1.74900223484405
-0.302676976651547
1.86822531243473
5.14212695982147
-0.11134249999139
-0.851495053263853
-3.32267426707525

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 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 time4 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318458&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]

4 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=318458&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318458&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	4 seconds
R Server	Big Analytics Cloud Computing Center

Central Tendency - Ungrouped Data
Measure	Value	S.E.	Value/S.E.
Arithmetic Mean	-9.27795e-16	0.677349	-1.36974e-15
Geometric Mean	NaN
Harmonic Mean	-0.732628
Quadratic Mean	2.9525
Winsorized Mean ( 1 / 6 )	-0.0220837	0.648862	-0.0340345
Winsorized Mean ( 2 / 6 )	-0.103655	0.601027	-0.172464
Winsorized Mean ( 3 / 6 )	-0.524616	0.379731	-1.38155
Winsorized Mean ( 4 / 6 )	-0.580112	0.32791	-1.76912
Winsorized Mean ( 5 / 6 )	-0.530511	0.23765	-2.23232
Winsorized Mean ( 6 / 6 )	-0.683464	0.13059	-5.23368
Trimmed Mean ( 1 / 6 )	-0.177528	0.603547	-0.294142
Trimmed Mean ( 2 / 6 )	-0.371834	0.50768	-0.732419
Trimmed Mean ( 3 / 6 )	-0.563391	0.354862	-1.58764
Trimmed Mean ( 4 / 6 )	-0.584932	0.304983	-1.91792
Trimmed Mean ( 5 / 6 )	-0.587342	0.238801	-2.45954
Trimmed Mean ( 6 / 6 )	-0.615758	0.181617	-3.39041
Median	-0.46542
Midrange	1.59776
Midmean - Weighted Average at Xnp	-0.757858
Midmean - Weighted Average at X(n+1)p	-0.587342
Midmean - Empirical Distribution Function	-0.757858
Midmean - Empirical Distribution Function - Averaging	-0.587342
Midmean - Empirical Distribution Function - Interpolation	-0.587342
Midmean - Closest Observation	-0.757858
Midmean - True Basic - Statistics Graphics Toolkit	-0.587342
Midmean - MS Excel (old versions)	-0.584932
Number of observations	20

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & -9.27795e-16 & 0.677349 & -1.36974e-15 \tabularnewline
Geometric Mean & NaN &  &  \tabularnewline
Harmonic Mean & -0.732628 &  &  \tabularnewline
Quadratic Mean & 2.9525 &  &  \tabularnewline
Winsorized Mean ( 1 / 6 ) & -0.0220837 & 0.648862 & -0.0340345 \tabularnewline
Winsorized Mean ( 2 / 6 ) & -0.103655 & 0.601027 & -0.172464 \tabularnewline
Winsorized Mean ( 3 / 6 ) & -0.524616 & 0.379731 & -1.38155 \tabularnewline
Winsorized Mean ( 4 / 6 ) & -0.580112 & 0.32791 & -1.76912 \tabularnewline
Winsorized Mean ( 5 / 6 ) & -0.530511 & 0.23765 & -2.23232 \tabularnewline
Winsorized Mean ( 6 / 6 ) & -0.683464 & 0.13059 & -5.23368 \tabularnewline
Trimmed Mean ( 1 / 6 ) & -0.177528 & 0.603547 & -0.294142 \tabularnewline
Trimmed Mean ( 2 / 6 ) & -0.371834 & 0.50768 & -0.732419 \tabularnewline
Trimmed Mean ( 3 / 6 ) & -0.563391 & 0.354862 & -1.58764 \tabularnewline
Trimmed Mean ( 4 / 6 ) & -0.584932 & 0.304983 & -1.91792 \tabularnewline
Trimmed Mean ( 5 / 6 ) & -0.587342 & 0.238801 & -2.45954 \tabularnewline
Trimmed Mean ( 6 / 6 ) & -0.615758 & 0.181617 & -3.39041 \tabularnewline
Median & -0.46542 &  &  \tabularnewline
Midrange & 1.59776 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & -0.757858 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & -0.587342 &  &  \tabularnewline
Midmean - Empirical Distribution Function & -0.757858 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & -0.587342 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & -0.587342 &  &  \tabularnewline
Midmean - Closest Observation & -0.757858 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & -0.587342 &  &  \tabularnewline
Midmean - MS Excel (old versions) & -0.584932 &  &  \tabularnewline
Number of observations & 20 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318458&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]-9.27795e-16[/C][C]0.677349[/C][C]-1.36974e-15[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]NaN[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]-0.732628[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]2.9525[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 6 )[/C][C]-0.0220837[/C][C]0.648862[/C][C]-0.0340345[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 6 )[/C][C]-0.103655[/C][C]0.601027[/C][C]-0.172464[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 6 )[/C][C]-0.524616[/C][C]0.379731[/C][C]-1.38155[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 6 )[/C][C]-0.580112[/C][C]0.32791[/C][C]-1.76912[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 6 )[/C][C]-0.530511[/C][C]0.23765[/C][C]-2.23232[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 6 )[/C][C]-0.683464[/C][C]0.13059[/C][C]-5.23368[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 6 )[/C][C]-0.177528[/C][C]0.603547[/C][C]-0.294142[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 6 )[/C][C]-0.371834[/C][C]0.50768[/C][C]-0.732419[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 6 )[/C][C]-0.563391[/C][C]0.354862[/C][C]-1.58764[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 6 )[/C][C]-0.584932[/C][C]0.304983[/C][C]-1.91792[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 6 )[/C][C]-0.587342[/C][C]0.238801[/C][C]-2.45954[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 6 )[/C][C]-0.615758[/C][C]0.181617[/C][C]-3.39041[/C][/ROW]
[ROW][C]Median[/C][C]-0.46542[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]1.59776[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]-0.757858[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]-0.587342[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]-0.757858[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]-0.587342[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]-0.587342[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]-0.757858[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]-0.587342[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]-0.584932[/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=318458&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318458&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	-9.27795e-16	0.677349	-1.36974e-15
Geometric Mean	NaN
Harmonic Mean	-0.732628
Quadratic Mean	2.9525
Winsorized Mean ( 1 / 6 )	-0.0220837	0.648862	-0.0340345
Winsorized Mean ( 2 / 6 )	-0.103655	0.601027	-0.172464
Winsorized Mean ( 3 / 6 )	-0.524616	0.379731	-1.38155
Winsorized Mean ( 4 / 6 )	-0.580112	0.32791	-1.76912
Winsorized Mean ( 5 / 6 )	-0.530511	0.23765	-2.23232
Winsorized Mean ( 6 / 6 )	-0.683464	0.13059	-5.23368
Trimmed Mean ( 1 / 6 )	-0.177528	0.603547	-0.294142
Trimmed Mean ( 2 / 6 )	-0.371834	0.50768	-0.732419
Trimmed Mean ( 3 / 6 )	-0.563391	0.354862	-1.58764
Trimmed Mean ( 4 / 6 )	-0.584932	0.304983	-1.91792
Trimmed Mean ( 5 / 6 )	-0.587342	0.238801	-2.45954
Trimmed Mean ( 6 / 6 )	-0.615758	0.181617	-3.39041
Median	-0.46542
Midrange	1.59776
Midmean - Weighted Average at Xnp	-0.757858
Midmean - Weighted Average at X(n+1)p	-0.587342
Midmean - Empirical Distribution Function	-0.757858
Midmean - Empirical Distribution Function - Averaging	-0.587342
Midmean - Empirical Distribution Function - Interpolation	-0.587342
Midmean - Closest Observation	-0.757858
Midmean - True Basic - Statistics Graphics Toolkit	-0.587342
Midmean - MS Excel (old versions)	-0.584932
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