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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 computationThu, 23 Oct 2008 11:33:01 -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/23/t1224783232hqlsy6ry1fixyqz.htm/, Retrieved Tue, 28 May 2024 16:26:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=18558, Retrieved Tue, 28 May 2024 16:26:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact153

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Central Tendency] [Q1 central tenden...] [2007-10-18 09:35:57] [b731da8b544846036771bbf9bf2f34ce]
F RMPD  [Univariate Explorative Data Analysis] [Model investering...] [2008-10-23 17:08:40] [e5d91604aae608e98a8ea24759233f66]
F    D      [Univariate Explorative Data Analysis] [Model omzet consu...] [2008-10-23 17:33:01] [55ca0ca4a201c9689dcf5fae352c92eb] [Current]
F RM          [Tukey lambda PPCC Plot] [PPCC plot Omzet c...] [2008-10-23 17:44:44] [e5d91604aae608e98a8ea24759233f66]
F    D          [Tukey lambda PPCC Plot] [PPCC plot Niet-we...] [2008-10-23 17:48:25] [e5d91604aae608e98a8ea24759233f66]
F    D            [Tukey lambda PPCC Plot] [PPCC plot BEL20 a...] [2008-10-23 17:50:22] [e5d91604aae608e98a8ea24759233f66]
Feedback Forum
2008-11-01 15:36:16 [Steffi Van Isveldt] [reply] 
Dit is een correcte berekening om de validiteit van het model na te gaan. De 4 assumpties worden gecontroleerd en correct geinterpreteerd. Zo kan je inderdaad zien bij de density plot dat de normaalverdeling een kleine afwijking vertoont.
2008-11-02 22:02:57 [Bernard Femont] [reply] 
De 4 assumpties worden gecontroleerd en correct geinterpreteerd. Zo kan je inderdaad zien bij de density plot dat de normaalverdeling een kleine afwijking vertoont.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
99.29
98.69
107.92
101.03
97.55
103.02
94.08
94.12
115.08
116.48
103.42
112.51
95.55
97.53
119.26
100.94
97.73
115.25
92.8
99.2
118.69
110.12
110.26
112.9
102.17
99.38
116.1
103.77
101.81
113.74
89.67
99.5
122.89
108.61
114.37
110.5
104.08
103.64
121.61
101.14
115.97
120.12
95.97
105.01
124.68
123.89
123.61
114.76
108.75
106.09
123.17
106.16
115.18
120.6
109.48
114.44
121.44
129.48
124.32
112.59

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'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=18558&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=18558&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18558&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'Gwilym Jenkins' @ 72.249.127.135






Descriptive Statistics
# observations60
minimum89.67
Q1101.0075
median109.115
mean109.035166666667
Q3116.0025
maximum129.48

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 60 \tabularnewline
minimum & 89.67 \tabularnewline
Q1 & 101.0075 \tabularnewline
median & 109.115 \tabularnewline
mean & 109.035166666667 \tabularnewline
Q3 & 116.0025 \tabularnewline
maximum & 129.48 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18558&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]89.67[/C][/ROW]
[ROW][C]Q1[/C][C]101.0075[/C][/ROW]
[ROW][C]median[/C][C]109.115[/C][/ROW]
[ROW][C]mean[/C][C]109.035166666667[/C][/ROW]
[ROW][C]Q3[/C][C]116.0025[/C][/ROW]
[ROW][C]maximum[/C][C]129.48[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=18558&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18558&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
minimum89.67
Q1101.0075
median109.115
mean109.035166666667
Q3116.0025
maximum129.48


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 12 ;
Parameters (R input):
par1 = 0 ; par2 = 12 ;
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')