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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 computationFri, 23 Dec 2011 11:57:03 -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/23/t1324659482seseon8x0meo9rt.htm/, Retrieved Mon, 29 Apr 2024 19:02:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160583, Retrieved Mon, 29 Apr 2024 19:02:53 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact96
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] [Workshop 7: Run s...] [2011-11-20 23:47:20] [a9a952c1cbc7081c25fad93a34aab827]
- R  D      [Univariate Explorative Data Analysis] [PAPER: hall of fa...] [2011-12-23 16:57:03] [6baf48ba14bcb50d9e72b77bece8a45b] [Current]
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Dataseries X:
279055
212408
233939
222117
189911
70849
605767
33186
227332
267925
371987
264989
212638
368577
269455
398124
335567
428322
182016
267365
279428
508849
217270
200004
257139
270941
324969
329962
190867
393860
327660
269239
396136
130446
430118
273950
428077
254312
120351
395658
345875
216827
224524
182485
157164
459455
78800
255072
368086
230299
244782
24188
400109
65029
101097
309810
375638
367127
381998
280106
400971
315924
291391
295075
280018
267432
217181
258166
264771
182961
256967
73566
272362
229056
229851
371391
398210
220419
231884
219381
206169
483074
146100
295224
80953
217384
179344
415550
389059
180679
299505
292260
199481
282361
329281
234577
297995
342490
416463
415683
297080
331792
229772
43287
238089
263322
302082
321797
193926
175138
354041
303273
23668
196743
61857
217543
440711
21054
252805
31961
360436
251948
187320
180842
38214
280392
358276
211775
447335
348017
441946
215177
130177
318037
466139
162279
416643
178322
292443
283913
244931
387072
246963
173260
346748
178402
268750
314070
1
14688
98
455
0
0
291847
415421
0
203
7199
46660
17547
121550
969
242774




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 1 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160583&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160583&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160583&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 time1 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Descriptive Statistics
# observations164
minimum0
Q1182367.75
median257053
mean248042.835365854
Q3330419.5
maximum605767

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 164 \tabularnewline
minimum & 0 \tabularnewline
Q1 & 182367.75 \tabularnewline
median & 257053 \tabularnewline
mean & 248042.835365854 \tabularnewline
Q3 & 330419.5 \tabularnewline
maximum & 605767 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160583&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]182367.75[/C][/ROW]
[ROW][C]median[/C][C]257053[/C][/ROW]
[ROW][C]mean[/C][C]248042.835365854[/C][/ROW]
[ROW][C]Q3[/C][C]330419.5[/C][/ROW]
[ROW][C]maximum[/C][C]605767[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160583&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160583&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
Q1182367.75
median257053
mean248042.835365854
Q3330419.5
maximum605767



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
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')