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Description of Statistical Computation

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 computationWed, 28 Oct 2009 12:26:25 -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/28/t1256754449efvoyy875nz9b10.htm/, Retrieved Sat, 16 May 2026 13:11:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51702, Retrieved Sat, 16 May 2026 13:11:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact385

Tree of Dependent Computations

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]
-   PD  [Bivariate Data Series] [WS 4- gegeven model] [2009-10-28 15:05:08] [af2352cd9a951bedd08ebe247d0de1a2]
- RMP     [Bivariate Explorative Data Analysis] [WS4- Deel 1: Vraag 1] [2009-10-28 16:22:04] [af2352cd9a951bedd08ebe247d0de1a2]
-    D      [Bivariate Explorative Data Analysis] [WS4- Deel 2: Vraag 1] [2009-10-28 17:51:17] [af2352cd9a951bedd08ebe247d0de1a2]
-    D        [Bivariate Explorative Data Analysis] [WS4- Deel 2: Vraag 2] [2009-10-28 18:18:44] [af2352cd9a951bedd08ebe247d0de1a2]
- RMPD            [Univariate Explorative Data Analysis] [WS4- Deel 2: Vraag 2] [2009-10-28 18:26:25] [d9efc2d105d810fc0b0ac636e31105d1] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
17.67
20.39
33.95
28.62
-23.14
50.94
-3.57
-5.44
45.55
33.28
-23.61
16.90
11.76
-11.87
38.71
-1.64
-22.84
41.92
-11.35
-0.35
12.26
-33.21
-7.26
17.01
-17.63
-12.73
19.32
-5.19
-55.72
7.56
9.46
15.64
-25.71
-30.65
14.46
-35.94
11.50
-7.91
15.25
-23.34
2.60
-21.44
4.43
46.57
-8.38
6.53
-15.34
-41.16
-1.45
-8.12
-47.65
20.35
-38.16
-11.40
-29.89
18.87
-8.68
29.00
-13.08
10.70

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51702&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51702&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51702&T=0

As an alternative you can also use a QR Code:  
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'George Udny Yule' @ 72.249.76.132






Descriptive Statistics
# observations60
minimum-55.72
Q1-15.9125
median-1.545
mean-0.0441666666666663
Q316.9275
maximum50.94

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 60 \tabularnewline
minimum & -55.72 \tabularnewline
Q1 & -15.9125 \tabularnewline
median & -1.545 \tabularnewline
mean & -0.0441666666666663 \tabularnewline
Q3 & 16.9275 \tabularnewline
maximum & 50.94 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51702&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]-55.72[/C][/ROW]
[ROW][C]Q1[/C][C]-15.9125[/C][/ROW]
[ROW][C]median[/C][C]-1.545[/C][/ROW]
[ROW][C]mean[/C][C]-0.0441666666666663[/C][/ROW]
[ROW][C]Q3[/C][C]16.9275[/C][/ROW]
[ROW][C]maximum[/C][C]50.94[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51702&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51702&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Descriptive Statistics
# observations60
minimum-55.72
Q1-15.9125
median-1.545
mean-0.0441666666666663
Q316.9275
maximum50.94


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 2 ;
Parameters (R input):
par1 = 0 ; par2 = 2 ;
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')