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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 02:15:39 +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/t12923792000zexcgw46n9f8hs.htm/, Retrieved Fri, 03 May 2024 06:02:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=110303, Retrieved Fri, 03 May 2024 06:02:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact164

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 02:15:39] [c6b3e187a4a1689d42fffda4bc860ab5] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
42
624
180
35
392
63
230
112
281
42
42
120
148
16
310
28
68
336
50
267
19
12
120
140
170
17
115
31
21
52
164
225
225
151
60
200
46
210
14

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=110303&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=110303&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110303&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
minimum12
Q142
median115
mean137.897435897436
Q3205
maximum624

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 39 \tabularnewline
minimum & 12 \tabularnewline
Q1 & 42 \tabularnewline
median & 115 \tabularnewline
mean & 137.897435897436 \tabularnewline
Q3 & 205 \tabularnewline
maximum & 624 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110303&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]39[/C][/ROW]
[ROW][C]minimum[/C][C]12[/C][/ROW]
[ROW][C]Q1[/C][C]42[/C][/ROW]
[ROW][C]median[/C][C]115[/C][/ROW]
[ROW][C]mean[/C][C]137.897435897436[/C][/ROW]
[ROW][C]Q3[/C][C]205[/C][/ROW]
[ROW][C]maximum[/C][C]624[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110303&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110303&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
minimum12
Q142
median115
mean137.897435897436
Q3205
maximum624


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')