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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 computationTue, 20 Oct 2009 11:08:04 -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/2009/Oct/20/t12560585400s58iyffcwdslth.htm/, Retrieved Thu, 02 May 2024 22:48:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=48831, Retrieved Thu, 02 May 2024 22:48:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact150

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
F RMPD  [Univariate Explorative Data Analysis] [Colombia Coffee] [2008-01-07 14:21:11] [74be16979710d4c4e7c6647856088456]
F RMPD    [Univariate Data Series] [] [2009-10-14 08:30:28] [74be16979710d4c4e7c6647856088456]
- RMPD      [Central Tendency] [WS3 Part2 Vraag1] [2009-10-18 08:54:29] [42ad1186d39724f834063794eac7cea3]
- RMP           [Univariate Explorative Data Analysis] [WS3 Part2 Vraag1 TVD] [2009-10-20 17:08:04] [37de18e38c1490dd77c2b362ed87f3bb] [Current]
- RMPD            [Central Tendency] [WS3 Part2 Vraag1 C] [2009-10-20 17:29:56] [42ad1186d39724f834063794eac7cea3]
-                   [Central Tendency] [WS3 part 2 vraag 2c] [2009-10-21 06:17:21] [f5d341d4bbba73282fc6e80153a6d315]
-                   [Central Tendency] [BDM 6] [2009-10-21 08:35:49] [f5d341d4bbba73282fc6e80153a6d315]
-  M                  [Central Tendency] [bart] [2010-01-25 07:18:59] [f5d341d4bbba73282fc6e80153a6d315]
-                   [Central Tendency] [TG 6] [2009-10-21 09:00:29] [a21bac9c8d3d56fdec8be4e719e2c7ed]
- RMPD            [Central Tendency] [WS3 Part2 Vraag2 C] [2009-10-20 17:34:45] [42ad1186d39724f834063794eac7cea3]
-                   [Central Tendency] [WS3 part 2 vraag2c] [2009-10-21 06:21:43] [f5d341d4bbba73282fc6e80153a6d315]
-                   [Central Tendency] [BDM 9] [2009-10-21 08:39:33] [f5d341d4bbba73282fc6e80153a6d315]
-                   [Central Tendency] [TG 9] [2009-10-21 09:03:03] [a21bac9c8d3d56fdec8be4e719e2c7ed]
- RMPD            [Central Tendency] [WS3 Part2 Vraag3 C] [2009-10-20 17:38:31] [42ad1186d39724f834063794eac7cea3]
-                   [Central Tendency] [BDM 12] [2009-10-21 08:42:57] [f5d341d4bbba73282fc6e80153a6d315]
-                   [Central Tendency] [BDM 17] [2009-10-21 08:48:30] [f5d341d4bbba73282fc6e80153a6d315]
-                   [Central Tendency] [TG 12] [2009-10-21 09:05:37] [a21bac9c8d3d56fdec8be4e719e2c7ed]
-                   [Central Tendency] [TG 17] [2009-10-21 09:10:21] [a21bac9c8d3d56fdec8be4e719e2c7ed]
- RMPD            [Central Tendency] [WS3 Part2 Vraag4 C] [2009-10-20 17:41:57] [42ad1186d39724f834063794eac7cea3]
-                   [Central Tendency] [BDM 15] [2009-10-21 08:46:00] [f5d341d4bbba73282fc6e80153a6d315]
-                   [Central Tendency] [TG 15] [2009-10-21 09:08:14] [a21bac9c8d3d56fdec8be4e719e2c7ed]
-                 [Univariate Explorative Data Analysis] [WS3 part 2 vraag 1 b] [2009-10-21 06:16:04] [f5d341d4bbba73282fc6e80153a6d315]
-                 [Univariate Explorative Data Analysis] [BDM 5] [2009-10-21 08:34:32] [f5d341d4bbba73282fc6e80153a6d315]
-                 [Univariate Explorative Data Analysis] [TG 5] [2009-10-21 08:59:39] [a21bac9c8d3d56fdec8be4e719e2c7ed]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101.3
106.3
94
102.8
102
105.1
92.4
81.4
105.8
120.3
100.7
88.8
94.3
99.9
103.4
103.3
98.8
104.2
91.2
74.7
108.5
114.5
96.9
89.6
97.1
100.3
122.6
115.4
109
129.1
102.8
96.2
127.7
128.9
126.5
119.8
113.2
114.1
134.1
130
121.8
132.1
105.3
103
117.1
126.3
138.1
119.5
138
135.5
178.6
162.2
176.9
204.9
132.2
142.5
164.3
174.9
175.4
143

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time17 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 & 17 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=48831&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]17 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=48831&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=48831&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 time17 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Descriptive Statistics
# observations60
minimum74.7
Q1101.15
median113.65
mean118.976666666667
Q3130.525
maximum204.9

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 60 \tabularnewline
minimum & 74.7 \tabularnewline
Q1 & 101.15 \tabularnewline
median & 113.65 \tabularnewline
mean & 118.976666666667 \tabularnewline
Q3 & 130.525 \tabularnewline
maximum & 204.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=48831&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]74.7[/C][/ROW]
[ROW][C]Q1[/C][C]101.15[/C][/ROW]
[ROW][C]median[/C][C]113.65[/C][/ROW]
[ROW][C]mean[/C][C]118.976666666667[/C][/ROW]
[ROW][C]Q3[/C][C]130.525[/C][/ROW]
[ROW][C]maximum[/C][C]204.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=48831&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=48831&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
minimum74.7
Q1101.15
median113.65
mean118.976666666667
Q3130.525
maximum204.9


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')