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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_edauni.wasp
Title produced by softwareUnivariate Explorative Data Analysis
Date of computationMon, 19 Oct 2009 01:04:35 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Oct/19/t12559359192akz5dke9n9ewkr.htm/, Retrieved Thu, 31 Oct 2024 23:13:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=47539, Retrieved Thu, 31 Oct 2024 23:13:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact204
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  [Univariate Explorative Data Analysis] [Colombia Coffee] [2008-01-07 14:21:11] [74be16979710d4c4e7c6647856088456]
F RMPD    [Univariate Data Series] [] [2009-10-14 08:30:28] [74be16979710d4c4e7c6647856088456]
-   PD      [Univariate Data Series] [WS 3Yt] [2009-10-17 09:25:12] [6e4e01d7eb22a9f33d58ebb35753a195]
-   PD        [Univariate Data Series] [WS3 YT-XT] [2009-10-17 09:36:45] [6e4e01d7eb22a9f33d58ebb35753a195]
-   PD          [Univariate Data Series] [Yt/Xt CT] [2009-10-17 09:39:32] [6e4e01d7eb22a9f33d58ebb35753a195]
- RMPD            [Central Tendency] [Yt/Xt CT1] [2009-10-17 09:42:58] [6e4e01d7eb22a9f33d58ebb35753a195]
- RM                [Harrell-Davis Quantiles] [WS 3 3] [2009-10-17 12:16:24] [6e4e01d7eb22a9f33d58ebb35753a195]
- RMP                   [Univariate Explorative Data Analysis] [ws3 3] [2009-10-19 07:04:35] [2e4ef2c1b76db9b31c0a03b96e94ad77] [Current]
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Dataseries X:
1.03
1.05
1.09
1.11
1.13
1.17
1.20
1.23
1.26
1.29
1.28
1.28
1.26
1.25
1.21
1.22
1.21
1.18
1.17
1.19
1.19
1.19
1.16
1.15
1.11
1.07
1.06
1.07
1.05
1.06
1.05
1.04
1.03
0.97
0.95
0.93
0.87
0.83
0.82
0.83
0.86
0.86
0.83
0.83
0.83
0.81
0.79
0.79
0.74
0.72
0.71
0.72
0.75
0.76
0.76
0.73
0.72
0.71
0.70
0.67




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 4 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=47539&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=47539&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=47539&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 time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Descriptive Statistics
# observations60
minimum0.67
Q10.8175
median1.045
mean0.992166666666667
Q31.1725
maximum1.29

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 60 \tabularnewline
minimum & 0.67 \tabularnewline
Q1 & 0.8175 \tabularnewline
median & 1.045 \tabularnewline
mean & 0.992166666666667 \tabularnewline
Q3 & 1.1725 \tabularnewline
maximum & 1.29 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=47539&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]0.67[/C][/ROW]
[ROW][C]Q1[/C][C]0.8175[/C][/ROW]
[ROW][C]median[/C][C]1.045[/C][/ROW]
[ROW][C]mean[/C][C]0.992166666666667[/C][/ROW]
[ROW][C]Q3[/C][C]1.1725[/C][/ROW]
[ROW][C]maximum[/C][C]1.29[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=47539&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=47539&T=1

As an alternative you can also use a QR Code:  

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

Descriptive Statistics
# observations60
minimum0.67
Q10.8175
median1.045
mean0.992166666666667
Q31.1725
maximum1.29



Parameters (Session):
par1 = 0 ; par2 = 36 ;
Parameters (R input):
par1 = 0 ; par2 = 36 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
x <- as.ts(x)
library(lattice)
bitmap(file='pic1.png')
plot(x,type='l',main='Run Sequence Plot',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic2.png')
hist(x)
grid()
dev.off()
bitmap(file='pic3.png')
if (par1 > 0)
{
densityplot(~x,col='black',main=paste('Density Plot bw = ',par1),bw=par1)
} else {
densityplot(~x,col='black',main='Density Plot')
}
dev.off()
bitmap(file='pic4.png')
qqnorm(x)
qqline(x)
grid()
dev.off()
if (par2 > 0)
{
bitmap(file='lagplot1.png')
dum <- cbind(lag(x,k=1),x)
dum
dum1 <- dum[2:length(x),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main='Lag plot (k=1), lowess, and regression line')
lines(lowess(z))
abline(lm(z))
dev.off()
if (par2 > 1) {
bitmap(file='lagplotpar2.png')
dum <- cbind(lag(x,k=par2),x)
dum
dum1 <- dum[(par2+1):length(x),]
dum1
z <- as.data.frame(dum1)
z
mylagtitle <- 'Lag plot (k='
mylagtitle <- paste(mylagtitle,par2,sep='')
mylagtitle <- paste(mylagtitle,'), and lowess',sep='')
plot(z,main=mylagtitle)
lines(lowess(z))
dev.off()
}
bitmap(file='pic5.png')
acf(x,lag.max=par2,main='Autocorrelation Function')
grid()
dev.off()
}
summary(x)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Descriptive Statistics',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'# observations',header=TRUE)
a<-table.element(a,length(x))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'minimum',header=TRUE)
a<-table.element(a,min(x))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,quantile(x,0.25))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'median',header=TRUE)
a<-table.element(a,median(x))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
a<-table.element(a,mean(x))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,quantile(x,0.75))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'maximum',header=TRUE)
a<-table.element(a,max(x))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')