Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_meanplot.wasp
Title produced by softwareMean Plot
Date of computationThu, 11 Nov 2010 17:23:22 +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/Nov/11/t12894965266t9cth52rzif3ub.htm/, Retrieved Thu, 28 Mar 2024 12:13:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=93623, Retrieved Thu, 28 Mar 2024 12:13:56 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact115
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
F RMPD  [Mean Plot] [Colombia Coffee] [2008-01-07 13:38:24] [74be16979710d4c4e7c6647856088456]
- RMPD      [Mean Plot] [WS6 - Mini Tutori...] [2010-11-11 17:23:22] [ee4a783fb13f41eb2e9bc8a0c4f26279] [Current]
-    D        [Mean Plot] [WS6 - Mini Tutori...] [2010-11-16 18:14:14] [1f5baf2b24e732d76900bb8178fc04e7]
- RM          [Mean Plot] [] [2011-11-15 22:46:01] [19d77e37efa419fdc040c74a96874aff]
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Dataseries X:
2,4
2,4
2,5
2,6
2,4
2,6
2,4
2,3
2,4
2,4
2,4
2,4
2,4
2,4
2,4
2,4
2,5
2,1
2,1
2
2
2
1,9
1,9
2
1,8
1,6
1,3
1,4
1,4
1,5
1,7
1,6
1,5
1,6
1,5
1,1
1,1
1,1
1,4
1,3
1,4
1,3
1,1
1
0,9
0,8
0,8
0,8
0,8
1
1,1
1
0,9
1,1
1,2
1,2
1,4
1,5
1,7
1,9
1,9
1,9
1,7
1,7
2,1
2
2
2,5
2,4
2,5
2,5
2
1,9
2,2
2,7
3,1
2,8
2,6
2,3
2,2
2,2
2
2
2,6
2,5
2,5
2,3
2
1,9
2
2,1
2,1
2,3
2,3
2,3
2,1
2,4
2,5
2,1
1,8
1,9
1,9
2,1
2,2
2
2,2
2
1,9
1,6
1,7
2
2,5
2,4
2,3
2,3
2,1
2,4
2,2
2,4
1,9
2,1
2,1
2,1
2
2,1
2,2
2,2
2,6
2,5
2,3
2,2
2,4
2,3
2,2
2,5
2,5
2,5
2,4
2,3
1,7
1,6
1,9
1,9
1,8
1,8
1,9
1,9
1,9
1,9
1,8
1,7
2,1
2,6
3,1
3,1
3,2
3,3
3,6
3,3
3,7
4
4
3,8
3,6
3,2
2,1
1,6
1,1
1,2
0,6
0,6
0
-0,1
-0,6
-0,2
-0,3
-0,1
0,5
0,9




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=93623&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=93623&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=93623&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135



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+1))
darr <- array(NA,dim=c(par1,np+1))
ari <- array(0,dim=par1)
dx <- diff(x)
j <- 0
for (i in 1:n)
{
j = j + 1
ari[j] = ari[j] + 1
arr[j,ari[j]] <- x[i]
darr[j,ari[j]] <- dx[i]
if (j == par1) j = 0
}
ari
arr
darr
arr.mean <- array(NA,dim=par1)
arr.median <- array(NA,dim=par1)
arr.midrange <- array(NA,dim=par1)
for (j in 1:par1)
{
arr.mean[j] <- mean(arr[j,],na.rm=TRUE)
arr.median[j] <- median(arr[j,],na.rm=TRUE)
arr.midrange[j] <- (quantile(arr[j,],0.75,na.rm=TRUE) + quantile(arr[j,],0.25,na.rm=TRUE)) / 2
}
overall.mean <- mean(x)
overall.median <- median(x)
overall.midrange <- (quantile(x,0.75) + quantile(x,0.25)) / 2
bitmap(file='plot1.png')
plot(arr.mean,type='b',ylab='mean',main='Mean Plot',xlab='Periodic Index')
mtext(paste('#blocks = ',np))
abline(overall.mean,0)
dev.off()
bitmap(file='plot2.png')
plot(arr.median,type='b',ylab='median',main='Median Plot',xlab='Periodic Index')
mtext(paste('#blocks = ',np))
abline(overall.median,0)
dev.off()
bitmap(file='plot3.png')
plot(arr.midrange,type='b',ylab='midrange',main='Midrange Plot',xlab='Periodic Index')
mtext(paste('#blocks = ',np))
abline(overall.midrange,0)
dev.off()
bitmap(file='plot4.png')
z <- data.frame(t(arr))
names(z) <- c(1:par1)
(boxplot(z,notch=TRUE,col='grey',xlab='Periodic Index',ylab='Value',main='Notched Box Plots - Periodic Subseries'))
dev.off()
bitmap(file='plot4b.png')
z <- data.frame(t(darr))
names(z) <- c(1:par1)
(boxplot(z,notch=TRUE,col='grey',xlab='Periodic Index',ylab='Value',main='Notched Box Plots - Differenced Periodic Subseries'))
dev.off()
bitmap(file='plot5.png')
z <- data.frame(arr)
names(z) <- c(1:np)
(boxplot(z,notch=TRUE,col='grey',xlab='Block Index',ylab='Value',main='Notched Box Plots - Sequential Blocks'))
dev.off()
bitmap(file='plot6.png')
z <- data.frame(cbind(arr.mean,arr.median,arr.midrange))
names(z) <- list('mean','median','midrange')
(boxplot(z,notch=TRUE,col='grey',ylab='Overall Central Tendency',main='Notched Box Plots'))
dev.off()