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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 computationSun, 26 Oct 2008 13:04:49 -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/2008/Oct/26/t1225047910qs9wtt6z4kol9wo.htm/, Retrieved Sun, 19 May 2024 12:57:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=19025, Retrieved Sun, 19 May 2024 12:57:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact152

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Explorative Data Analysis] [Investigation Dis...] [2007-10-21 17:06:37] [b9964c45117f7aac638ab9056d451faa]
F    D  [Univariate Explorative Data Analysis] [] [2008-10-26 18:30:50] [29747f79f5beb5b2516e1271770ecb47]
F    D    [Univariate Explorative Data Analysis] [] [2008-10-26 19:01:54] [29747f79f5beb5b2516e1271770ecb47]
F    D        [Univariate Explorative Data Analysis] [] [2008-10-26 19:04:49] [c0a347e3519123f7eef62b705326dad9] [Current]
-               [Univariate Explorative Data Analysis] [] [2008-10-26 19:07:10] [29747f79f5beb5b2516e1271770ecb47]
F    D            [Univariate Explorative Data Analysis] [] [2008-10-26 19:09:23] [29747f79f5beb5b2516e1271770ecb47]
F    D              [Univariate Explorative Data Analysis] [] [2008-10-26 19:10:36] [29747f79f5beb5b2516e1271770ecb47]
Feedback Forum
2008-11-03 17:51:59 [Jeroen Michel] [reply] 
Wat Q7 betreft kunnen we wel degelijk stellen dat je de 4 reeksen test op de constant + random component. Verder wordt hier geen enkele conclusie uitgetrokken.

Wat Q10 betreft is de feedback zowat gelijkaardig. Je staaft de bevindingen enkel met de zaken die je waarneemt. Een andere grafiek en methode gebruiken had hierbij aangeraden geweest. Voorbeeld 'autocorrelation plot'.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
89,6
92,8
107,6
104,6
103,0
106,9
56,3
93,4
109,1
113,8
97,4
72,5
82,7
88,9
105,9
100,8
94,0
105,0
58,5
87,6
113,1
112,5
89,6
74,5
82,7
90,1
109,4
96,0
89,2
109,1
49,1
92,9
107,7
103,5
91,1
79,8
71,9
82,9
90,1
100,7
90,7
108,8
44,1
93,6
107,4
96,5
93,6
76,5
76,7
84,0
103,3
88,5
99,0
105,9
44,7
94,0
107,1
104,8
102,5
77,7
85,2
91,3
106,5
92,4
97,5
107,0
51,1
98,6
102,2
114,3
99,4
72,5
92,3
99,4
85,9
109,4
97,6
104,7
56,5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19025&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19025&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19025&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 time2 seconds
R Server'George Udny Yule' @ 72.249.76.132






Descriptive Statistics
# observations79
minimum44.1
Q185.55
median94
mean92.0189873417721
Q3104.75
maximum114.3

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 79 \tabularnewline
minimum & 44.1 \tabularnewline
Q1 & 85.55 \tabularnewline
median & 94 \tabularnewline
mean & 92.0189873417721 \tabularnewline
Q3 & 104.75 \tabularnewline
maximum & 114.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19025&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]79[/C][/ROW]
[ROW][C]minimum[/C][C]44.1[/C][/ROW]
[ROW][C]Q1[/C][C]85.55[/C][/ROW]
[ROW][C]median[/C][C]94[/C][/ROW]
[ROW][C]mean[/C][C]92.0189873417721[/C][/ROW]
[ROW][C]Q3[/C][C]104.75[/C][/ROW]
[ROW][C]maximum[/C][C]114.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19025&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19025&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
# observations79
minimum44.1
Q185.55
median94
mean92.0189873417721
Q3104.75
maximum114.3


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 0 ;
Parameters (R input):
par1 = 0 ; par2 = 0 ;
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)
grid()
dev.off()
if (par2 > 0)
{
bitmap(file='lagplot.png')
dum <- cbind(lag(x,k=1),x)
dum
dum1 <- dum[2:length(x),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Lag plot, lowess, and regression line'))
lines(lowess(z))
abline(lm(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')