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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 15:36:23 -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/t12560746771vpq2zfusljbh64.htm/, Retrieved Fri, 03 May 2024 02:18:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=49200, Retrieved Fri, 03 May 2024 02:18:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact184

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      [Univariate Explorative Data Analysis] [Workshop3 - histo...] [2009-10-20 19:05:41] [df6326eec97a6ca984a853b142930499]
-   PD          [Univariate Explorative Data Analysis] [workshop vraag 2....] [2009-10-20 21:36:23] [0cc924834281808eda7297686c82928f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
137.3
97.1
141.9
130.8
117.3
186.8
139.2
175.5
168
181.3
236.1
171.9
167.4
-5.9
-41.1
185.3
24.5
146.4
61.7
140
120.5
133.3
93.8
209.7
206.5
104.1
115
56.8
118.6
242.4
364.4
339.7
110.7
110.5
-57.3
-85
361.1
74.1
-8
201.5
-408.4
306.2
201.2
-82.3
135.3
18.6
-39.9
-280.7
223.2
31.4
-72.8
107.1
-106.3
120.2
22.9
47.1
-349.8
33.7
-7.4
-219.5
-61.4
27.7
-395.6
343.6
-302.6
-7.3
-22
-49.8
-77.3
173
-548.9
-80.4
236.6
250.6
254.2
351.6
-93.1
-72.1
454.5
252.1
253.6
437.9
344.7
307.1
431.5
474.9
-230.4
299.6
-254.1
320.3
558.7
340.2
874.2
162.5
382.3
65.7
678.7
18.1
663.6
101.7
371.7
165.7
469.5
299.5
397.8
692.8
358.9
127.5
311.2
263.5
309.1
376.4
237.2
441.5
377.5
266.9
320.6
610.7
515.6
429.5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=49200&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]4 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=49200&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=49200&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Descriptive Statistics
# observations120
minimum-548.9
Q124.1
median154.45
mean160.508333333333
Q3309.625
maximum874.2

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 120 \tabularnewline
minimum & -548.9 \tabularnewline
Q1 & 24.1 \tabularnewline
median & 154.45 \tabularnewline
mean & 160.508333333333 \tabularnewline
Q3 & 309.625 \tabularnewline
maximum & 874.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=49200&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]120[/C][/ROW]
[ROW][C]minimum[/C][C]-548.9[/C][/ROW]
[ROW][C]Q1[/C][C]24.1[/C][/ROW]
[ROW][C]median[/C][C]154.45[/C][/ROW]
[ROW][C]mean[/C][C]160.508333333333[/C][/ROW]
[ROW][C]Q3[/C][C]309.625[/C][/ROW]
[ROW][C]maximum[/C][C]874.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=49200&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=49200&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
# observations120
minimum-548.9
Q124.1
median154.45
mean160.508333333333
Q3309.625
maximum874.2


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 500 ; par2 = 12 ;
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')