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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

Mon, 23 Jan 2017 09:59:28 +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/2017/Jan/23/t1485161981jty8i8o7gwikez7.htm/, Retrieved Wed, 15 May 2024 22:06:54 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=304168, Retrieved Wed, 15 May 2024 22:06:54 +0000

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Original text written by user:

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User-defined keywords

Estimated Impact

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

-       [Central Tendency] [] [2017-01-23 08:59:28] [36884fbde1107444791dd71ee0072a5a] [Current]

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0.02618
-2.386
-2.386
-0.06051
3.949
2.305
9.563
-1.061
-2.14
2.506
-3.09
0.6693
-3.794
-2.587
-8.218
2.305
8.667
2.088
0.3076
-3.78

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=304168&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=304168&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304168&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

Central Tendency - Ungrouped Data
Measure	Value	S.E.	Value/S.E.
Arithmetic Mean	0.144179	0.936272	0.153992
Geometric Mean	NaN
Harmonic Mean	0.799143
Quadratic Mean	4.08366
Winsorized Mean ( 1 / 6 )	0.320578	0.830338	0.386082
Winsorized Mean ( 2 / 6 )	-0.149822	0.616324	-0.243089
Winsorized Mean ( 3 / 6 )	-0.262772	0.516181	-0.509069
Winsorized Mean ( 4 / 6 )	-0.202372	0.477162	-0.424115
Winsorized Mean ( 5 / 6 )	-0.152121	0.464187	-0.327716
Winsorized Mean ( 6 / 6 )	-0.217222	0.446264	-0.486755
Trimmed Mean ( 1 / 6 )	0.0854761	0.754957	0.11322
Trimmed Mean ( 2 / 6 )	-0.208402	0.592779	-0.351568
Trimmed Mean ( 3 / 6 )	-0.250245	0.546055	-0.458278
Trimmed Mean ( 4 / 6 )	-0.243286	0.540807	-0.449857
Trimmed Mean ( 5 / 6 )	-0.263743	0.543808	-0.484993
Trimmed Mean ( 6 / 6 )	-0.319554	0.525902	-0.60763
Median	-0.017165
Midrange	0.6725
Midmean - Weighted Average at Xnp	-0.243286
Midmean - Weighted Average at X(n+1)p	-0.0302209
Midmean - Empirical Distribution Function	-0.243286
Midmean - Empirical Distribution Function - Averaging	-0.0302209
Midmean - Empirical Distribution Function - Interpolation	-0.0302209
Midmean - Closest Observation	-0.243286
Midmean - True Basic - Statistics Graphics Toolkit	-0.0302209
Midmean - MS Excel (old versions)	-0.243286
Number of observations	20

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 0.144179 & 0.936272 & 0.153992 \tabularnewline
Geometric Mean & NaN &  &  \tabularnewline
Harmonic Mean & 0.799143 &  &  \tabularnewline
Quadratic Mean & 4.08366 &  &  \tabularnewline
Winsorized Mean ( 1 / 6 ) & 0.320578 & 0.830338 & 0.386082 \tabularnewline
Winsorized Mean ( 2 / 6 ) & -0.149822 & 0.616324 & -0.243089 \tabularnewline
Winsorized Mean ( 3 / 6 ) & -0.262772 & 0.516181 & -0.509069 \tabularnewline
Winsorized Mean ( 4 / 6 ) & -0.202372 & 0.477162 & -0.424115 \tabularnewline
Winsorized Mean ( 5 / 6 ) & -0.152121 & 0.464187 & -0.327716 \tabularnewline
Winsorized Mean ( 6 / 6 ) & -0.217222 & 0.446264 & -0.486755 \tabularnewline
Trimmed Mean ( 1 / 6 ) & 0.0854761 & 0.754957 & 0.11322 \tabularnewline
Trimmed Mean ( 2 / 6 ) & -0.208402 & 0.592779 & -0.351568 \tabularnewline
Trimmed Mean ( 3 / 6 ) & -0.250245 & 0.546055 & -0.458278 \tabularnewline
Trimmed Mean ( 4 / 6 ) & -0.243286 & 0.540807 & -0.449857 \tabularnewline
Trimmed Mean ( 5 / 6 ) & -0.263743 & 0.543808 & -0.484993 \tabularnewline
Trimmed Mean ( 6 / 6 ) & -0.319554 & 0.525902 & -0.60763 \tabularnewline
Median & -0.017165 &  &  \tabularnewline
Midrange & 0.6725 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & -0.243286 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & -0.0302209 &  &  \tabularnewline
Midmean - Empirical Distribution Function & -0.243286 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & -0.0302209 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & -0.0302209 &  &  \tabularnewline
Midmean - Closest Observation & -0.243286 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & -0.0302209 &  &  \tabularnewline
Midmean - MS Excel (old versions) & -0.243286 &  &  \tabularnewline
Number of observations & 20 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304168&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]0.144179[/C][C]0.936272[/C][C]0.153992[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]NaN[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]0.799143[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]4.08366[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 6 )[/C][C]0.320578[/C][C]0.830338[/C][C]0.386082[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 6 )[/C][C]-0.149822[/C][C]0.616324[/C][C]-0.243089[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 6 )[/C][C]-0.262772[/C][C]0.516181[/C][C]-0.509069[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 6 )[/C][C]-0.202372[/C][C]0.477162[/C][C]-0.424115[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 6 )[/C][C]-0.152121[/C][C]0.464187[/C][C]-0.327716[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 6 )[/C][C]-0.217222[/C][C]0.446264[/C][C]-0.486755[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 6 )[/C][C]0.0854761[/C][C]0.754957[/C][C]0.11322[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 6 )[/C][C]-0.208402[/C][C]0.592779[/C][C]-0.351568[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 6 )[/C][C]-0.250245[/C][C]0.546055[/C][C]-0.458278[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 6 )[/C][C]-0.243286[/C][C]0.540807[/C][C]-0.449857[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 6 )[/C][C]-0.263743[/C][C]0.543808[/C][C]-0.484993[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 6 )[/C][C]-0.319554[/C][C]0.525902[/C][C]-0.60763[/C][/ROW]
[ROW][C]Median[/C][C]-0.017165[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]0.6725[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]-0.243286[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]-0.0302209[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]-0.243286[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]-0.0302209[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]-0.0302209[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]-0.243286[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]-0.0302209[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]-0.243286[/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=304168&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304168&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	0.144179	0.936272	0.153992
Geometric Mean	NaN
Harmonic Mean	0.799143
Quadratic Mean	4.08366
Winsorized Mean ( 1 / 6 )	0.320578	0.830338	0.386082
Winsorized Mean ( 2 / 6 )	-0.149822	0.616324	-0.243089
Winsorized Mean ( 3 / 6 )	-0.262772	0.516181	-0.509069
Winsorized Mean ( 4 / 6 )	-0.202372	0.477162	-0.424115
Winsorized Mean ( 5 / 6 )	-0.152121	0.464187	-0.327716
Winsorized Mean ( 6 / 6 )	-0.217222	0.446264	-0.486755
Trimmed Mean ( 1 / 6 )	0.0854761	0.754957	0.11322
Trimmed Mean ( 2 / 6 )	-0.208402	0.592779	-0.351568
Trimmed Mean ( 3 / 6 )	-0.250245	0.546055	-0.458278
Trimmed Mean ( 4 / 6 )	-0.243286	0.540807	-0.449857
Trimmed Mean ( 5 / 6 )	-0.263743	0.543808	-0.484993
Trimmed Mean ( 6 / 6 )	-0.319554	0.525902	-0.60763
Median	-0.017165
Midrange	0.6725
Midmean - Weighted Average at Xnp	-0.243286
Midmean - Weighted Average at X(n+1)p	-0.0302209
Midmean - Empirical Distribution Function	-0.243286
Midmean - Empirical Distribution Function - Averaging	-0.0302209
Midmean - Empirical Distribution Function - Interpolation	-0.0302209
Midmean - Closest Observation	-0.243286
Midmean - True Basic - Statistics Graphics Toolkit	-0.0302209
Midmean - MS Excel (old versions)	-0.243286
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