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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, 08 Dec 2011 06:05:55 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/08/t1323342390jatnczia1wf1kfy.htm/, Retrieved Fri, 03 May 2024 08:26:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=152830, Retrieved Fri, 03 May 2024 08:26:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [time effect in su...] [2010-11-17 08:55:33] [b98453cac15ba1066b407e146608df68]
- R PD    [Univariate Explorative Data Analysis] [Paper: multiple r...] [2011-12-08 11:05:55] [e889f2ef2eeddd5259af4a52678400a6] [Current]
-  MP       [Univariate Explorative Data Analysis] [Paper: Run Sequence] [2011-12-08 11:18:41] [1321c14511baa35aebbc5dda661708fe]
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Dataseries X:
236496
130631
198514
189326
137449
65295
439387
33186
174859
186657
261949
190794
138866
296878
192648
333348
242212
263451
150733
223226
240028
384138
156540
148421
176502
191441
249735
236812
142329
259667
228871
176054
286683
87485
322865
247013
340093
191653
114673
284210
284195
155363
174198
142986
140319
392666
78800
201970
302674
164733
194221
24188
340411
65029
101097
243889
273003
282220
273495
214872
333165
260981
184474
222366
205675
201345
163043
204250
197760
127260
216092
73566
213198
177949
148698
300103
251437
191971
154651
155473
132672
376465
145869
223666
80953
130789
135042
300074
271757
150949
216802
197389
156583
222599
261601
178489
200657
259084
302789
342025
246440
251306
159965
43287
172212
181781
227681
260464
106288
109632
268905
266568
23623
152474
61857
144889
330910
21054
223718
31414
259747
190495
154984
112933
38214
158671
299775
172783
348678
266701
358933
172464
94381
243875
382487
111853
334926
147979
216638
192853
173710
336678
212961
173260
271773
127096
203606
230177
1
14688
98
455
0
0
195765
306514
0
203
7199
46660
17547
105044
969
165838




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152830&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152830&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152830&T=0

As an alternative you can also use a 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 time2 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Descriptive Statistics
# observations164
minimum0
Q1138511.75
median191547
mean189056.329268293
Q3259229.75
maximum439387

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 164 \tabularnewline
minimum & 0 \tabularnewline
Q1 & 138511.75 \tabularnewline
median & 191547 \tabularnewline
mean & 189056.329268293 \tabularnewline
Q3 & 259229.75 \tabularnewline
maximum & 439387 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152830&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]164[/C][/ROW]
[ROW][C]minimum[/C][C]0[/C][/ROW]
[ROW][C]Q1[/C][C]138511.75[/C][/ROW]
[ROW][C]median[/C][C]191547[/C][/ROW]
[ROW][C]mean[/C][C]189056.329268293[/C][/ROW]
[ROW][C]Q3[/C][C]259229.75[/C][/ROW]
[ROW][C]maximum[/C][C]439387[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152830&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152830&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Descriptive Statistics
# observations164
minimum0
Q1138511.75
median191547
mean189056.329268293
Q3259229.75
maximum439387



Parameters (Session):
par1 = 0 ; par2 = 0 ;
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')