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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, 05 Nov 2009 02:26:44 -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/05/t1257413271wp8ee1qw11ot5qe.htm/, Retrieved Thu, 02 May 2024 18:53:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53901, Retrieved Thu, 02 May 2024 18:53:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144

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]
- RMPD    [Univariate Explorative Data Analysis] [Run sequence uitv...] [2009-11-05 09:26:44] [9be6fbb216efe5bb8ca600257c6e1971] [Current]
-           [Univariate Explorative Data Analysis] [tg4] [2009-11-10 14:29:14] [a21bac9c8d3d56fdec8be4e719e2c7ed]
- R           [Univariate Explorative Data Analysis] [ws6] [2009-11-15 22:20:35] [3fc64fd7a52ce121dfe13dba27bf6e5b]
-           [Univariate Explorative Data Analysis] [tvd4] [2009-11-10 14:55:18] [42ad1186d39724f834063794eac7cea3]
-             [Univariate Explorative Data Analysis] [PA4] [2009-12-15 09:56:03] [a21bac9c8d3d56fdec8be4e719e2c7ed]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
98.41
85.90
95.82
92.56
94.39
100.13
91.25
80.19
95.15
110.43
85.55
66.89
79.93
79.84
83.36
82.29
73.23
74.60
77.40
67.59
89.97
94.45
85.08
70.70
72.94
77.08
90.35
104.03
90.17
104.79
100.54
83.65
104.52
119.66
104.70
100.89
110.43
103.36
113.46
113.55
106.56
116.52
101.87
111.16
114.62
106.36
124.87
102.46
129.96
141.66
162.42
157.73
177.29
197.46
160.53
146.96
171.94
198.77
191.38
164.02

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53901&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]7 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=53901&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53901&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 time7 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Descriptive Statistics
# observations60
minimum66.89
Q185.4325
median101.38
mean108.896166666667
Q3115.095
maximum198.77

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 60 \tabularnewline
minimum & 66.89 \tabularnewline
Q1 & 85.4325 \tabularnewline
median & 101.38 \tabularnewline
mean & 108.896166666667 \tabularnewline
Q3 & 115.095 \tabularnewline
maximum & 198.77 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53901&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]66.89[/C][/ROW]
[ROW][C]Q1[/C][C]85.4325[/C][/ROW]
[ROW][C]median[/C][C]101.38[/C][/ROW]
[ROW][C]mean[/C][C]108.896166666667[/C][/ROW]
[ROW][C]Q3[/C][C]115.095[/C][/ROW]
[ROW][C]maximum[/C][C]198.77[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53901&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53901&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
minimum66.89
Q185.4325
median101.38
mean108.896166666667
Q3115.095
maximum198.77


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')