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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, 15 Dec 2010 02:13:50 +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/Dec/15/t1292379093grg1r4031x0h7tf.htm/, Retrieved Fri, 03 May 2024 06:47:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=110301, Retrieved Fri, 03 May 2024 06:47:35 +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)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
-   PD  [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-14 19:55:28] [c2a9e95daa10045f9fd6252038bcb219]
- RMPD    [Kendall tau Correlation Matrix] [Sleep] [2010-12-14 21:45:48] [c2a9e95daa10045f9fd6252038bcb219]
- RMPD      [Univariate Explorative Data Analysis] [EDA-weight] [2010-12-15 00:55:49] [d672a41e0af7ff107c03f1d65e47fd32]
-    D        [Univariate Explorative Data Analysis] [EDA-lichaamsgewicht] [2010-12-15 01:05:39] [d672a41e0af7ff107c03f1d65e47fd32]
-    D          [Univariate Explorative Data Analysis] [EDA-hersengewicht] [2010-12-15 01:08:41] [d672a41e0af7ff107c03f1d65e47fd32]
- RMPD            [Kendall tau Correlation Matrix] [Pearson Correlati...] [2010-12-15 01:18:33] [c2a9e95daa10045f9fd6252038bcb219]
- RM D                [Univariate Explorative Data Analysis] [] [2010-12-15 02:13:50] [c6b3e187a4a1689d42fffda4bc860ab5] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2
1.8
0.7
3.9
1
3.6
1.4
1.5
0.7
2.1
4.1
1.2
0.5
3.4
1.5
3.4
0.8
0.8
2
1.9
1.3
5.6
3.1
1.8
0.9
1.8
1.9
0.9
2.6
2.4
1.2
0.9
0.5
0.6
2.3
0.5
2.6
0.6
6.6

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110301&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]3 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=110301&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110301&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 time3 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Descriptive Statistics
# observations39
minimum0.5
Q10.9
median1.8
mean1.95897435897436
Q32.5
maximum6.6

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 39 \tabularnewline
minimum & 0.5 \tabularnewline
Q1 & 0.9 \tabularnewline
median & 1.8 \tabularnewline
mean & 1.95897435897436 \tabularnewline
Q3 & 2.5 \tabularnewline
maximum & 6.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110301&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]39[/C][/ROW]
[ROW][C]minimum[/C][C]0.5[/C][/ROW]
[ROW][C]Q1[/C][C]0.9[/C][/ROW]
[ROW][C]median[/C][C]1.8[/C][/ROW]
[ROW][C]mean[/C][C]1.95897435897436[/C][/ROW]
[ROW][C]Q3[/C][C]2.5[/C][/ROW]
[ROW][C]maximum[/C][C]6.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110301&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110301&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
# observations39
minimum0.5
Q10.9
median1.8
mean1.95897435897436
Q32.5
maximum6.6


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
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)
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')