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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, 04 Nov 2009 05:01:28 -0700
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/Nov/04/t1257336120yyxg2cqhb3jnqsb.htm/, Retrieved Mon, 29 Apr 2024 10:10:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53578, Retrieved Mon, 29 Apr 2024 10:10:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact139

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Notched Boxplots] [3/11/2009] [2009-11-02 21:10:41] [b98453cac15ba1066b407e146608df68]
-    D  [Notched Boxplots] [ws 6 1] [2009-11-04 11:54:28] [6e4e01d7eb22a9f33d58ebb35753a195]
- RMPD    [Univariate Explorative Data Analysis] [ws 6 kla] [2009-11-04 11:57:24] [6e4e01d7eb22a9f33d58ebb35753a195]
-           [Univariate Explorative Data Analysis] [ws 6 sto] [2009-11-04 11:59:43] [6e4e01d7eb22a9f33d58ebb35753a195]
-    D          [Univariate Explorative Data Analysis] [ws 6 sch] [2009-11-04 12:01:28] [2e4ef2c1b76db9b31c0a03b96e94ad77] [Current]
-    D            [Univariate Explorative Data Analysis] [ws 6 totpr] [2009-11-04 12:02:45] [6e4e01d7eb22a9f33d58ebb35753a195]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
103,34
103,38
103,64
104,04
104,11
104,11
104,11
104,17
105,16
105,86
106,11
106,11
106,11
106,13
106,67
106,85
106,97
107,02
107,02
107,07
107,76
108,10
108,18
108,22
108,22
108,17
108,31
108,31
108,36
108,46
108,46
108,46
109,43
109,55
109,62
109,70
109,70
109,56
109,92
109,81
109,78
109,80
109,80
109,79
110,40
110,95
111,07
111,09
111,10
111,01
111,01
111,35
111,42
111,24
111,24
111,47
111,57
111,96
112,02
112,02

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53578&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]5 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=53578&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53578&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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Descriptive Statistics
# observations60
minimum103.34
Q1106.805
median108.46
mean108.406166666667
Q3110.5375
maximum112.02

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 60 \tabularnewline
minimum & 103.34 \tabularnewline
Q1 & 106.805 \tabularnewline
median & 108.46 \tabularnewline
mean & 108.406166666667 \tabularnewline
Q3 & 110.5375 \tabularnewline
maximum & 112.02 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53578&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]103.34[/C][/ROW]
[ROW][C]Q1[/C][C]106.805[/C][/ROW]
[ROW][C]median[/C][C]108.46[/C][/ROW]
[ROW][C]mean[/C][C]108.406166666667[/C][/ROW]
[ROW][C]Q3[/C][C]110.5375[/C][/ROW]
[ROW][C]maximum[/C][C]112.02[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53578&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53578&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
minimum103.34
Q1106.805
median108.46
mean108.406166666667
Q3110.5375
maximum112.02


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 36 ;
Parameters (R input):
par1 = 0 ; par2 = 36 ;
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')