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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_edabi.wasp
Title produced by softwareBivariate Explorative Data Analysis
Date of computationWed, 04 Nov 2009 11:52:50 -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/04/t12573608084105pyl059g6xkh.htm/, Retrieved Mon, 29 Apr 2024 11:11:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53800, Retrieved Mon, 29 Apr 2024 11:11:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsRob_WS5
Estimated Impact132
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Explorative Data Analysis] [Workshop 5] [2009-10-28 17:21:02] [03557919bc1ce1475f4920f6a43c36b0]
-    D  [Bivariate Explorative Data Analysis] [SHW_W5.3] [2009-10-30 14:41:18] [f966872135bb25240f339c0c372beeec]
-  M D      [Bivariate Explorative Data Analysis] [] [2009-11-04 18:52:50] [9002751dd674b8c934bf183fdf4510e9] [Current]
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Dataseries X:
106370
109375
116476
123297
114813
117925
126466
131235
120546
123791
129813
133463
122987
125418
130199
133016
121454
122044
128313
131556
120027
123001
130111
132524
123742
124931
133646
136557
127509
128945
137191
139716
129083
131604
139413
143125
133948
137116
144864
149277
138796
143258
150034
154708
144888
148762
156500
161088
152772
158011
163318
169969
162269
165765
170600
174681
166364
170240
176150
182056
172218
177856
182253
188090
176863
183273
187969
194650
183036
189516
193805
200499
188142
193732
197126
205140
191751
196700
199784
207360
196101
200824
205743
212489
200810
203683
207286
210910
194915
217920
Dataseries Y:
1844
1790
1707
1617
1530
1453
1386
1329
1255
1201
1179
1167
1160
1161
1203
1290
1404
1550
1670
1746
1817
1836
1872
1956
2010
1933
1917
1900
1829
1805
1730
1650
1618
1582
1576
1512
1540
1519
1473
1402
1299
1267
1200
1145
1103
1076
1095
1080
1082
1060
1027
1003
987
959
913
912
889
882
901
913
921
914
950
894
930
890
898
880
840
845
819
838
833
837
850
927
932
978
978
974
977
950
935
913
942
990
1075
1181
1336
1492




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53800&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53800&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53800&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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Model: Y[t] = c + b X[t] + e[t]
c2601.20412342194
b-0.0085513961510827

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 2601.20412342194 \tabularnewline
b & -0.0085513961510827 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53800&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]2601.20412342194[/C][/ROW]
[ROW][C]b[/C][C]-0.0085513961510827[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53800&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53800&T=1

As an alternative you can also use a QR Code:  

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

Model: Y[t] = c + b X[t] + e[t]
c2601.20412342194
b-0.0085513961510827







Descriptive Statistics about e[t]
# observations90
minimum-389.493564988729
Q1-167.504721726864
median-77.4471414356441
mean-9.12603326241879e-15
Q3130.145797445648
maximum754.316125822004

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 90 \tabularnewline
minimum & -389.493564988729 \tabularnewline
Q1 & -167.504721726864 \tabularnewline
median & -77.4471414356441 \tabularnewline
mean & -9.12603326241879e-15 \tabularnewline
Q3 & 130.145797445648 \tabularnewline
maximum & 754.316125822004 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53800&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]90[/C][/ROW]
[ROW][C]minimum[/C][C]-389.493564988729[/C][/ROW]
[ROW][C]Q1[/C][C]-167.504721726864[/C][/ROW]
[ROW][C]median[/C][C]-77.4471414356441[/C][/ROW]
[ROW][C]mean[/C][C]-9.12603326241879e-15[/C][/ROW]
[ROW][C]Q3[/C][C]130.145797445648[/C][/ROW]
[ROW][C]maximum[/C][C]754.316125822004[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53800&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53800&T=2

As an alternative you can also use a QR Code:  

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

Descriptive Statistics about e[t]
# observations90
minimum-389.493564988729
Q1-167.504721726864
median-77.4471414356441
mean-9.12603326241879e-15
Q3130.145797445648
maximum754.316125822004



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)
y <- as.ts(y)
mylm <- lm(y~x)
cbind(mylm$resid)
library(lattice)
bitmap(file='pic1.png')
plot(y,type='l',main='Run Sequence Plot of Y[t]',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic1a.png')
plot(x,type='l',main='Run Sequence Plot of X[t]',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic1b.png')
plot(x,y,main='Scatter Plot',xlab='X[t]',ylab='Y[t]')
grid()
dev.off()
bitmap(file='pic1c.png')
plot(mylm$resid,type='l',main='Run Sequence Plot of e[t]',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic2.png')
hist(mylm$resid,main='Histogram of e[t]')
dev.off()
bitmap(file='pic3.png')
if (par1 > 0)
{
densityplot(~mylm$resid,col='black',main=paste('Density Plot of e[t] bw = ',par1),bw=par1)
} else {
densityplot(~mylm$resid,col='black',main='Density Plot of e[t]')
}
dev.off()
bitmap(file='pic4.png')
qqnorm(mylm$resid,main='QQ plot of e[t]')
qqline(mylm$resid)
grid()
dev.off()
if (par2 > 0)
{
bitmap(file='pic5.png')
acf(mylm$resid,lag.max=par2,main='Residual Autocorrelation Function')
grid()
dev.off()
}
summary(x)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Model: Y[t] = c + b X[t] + e[t]',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'c',1,TRUE)
a<-table.element(a,mylm$coeff[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'b',1,TRUE)
a<-table.element(a,mylm$coeff[[2]])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Descriptive Statistics about e[t]',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'# observations',header=TRUE)
a<-table.element(a,length(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'minimum',header=TRUE)
a<-table.element(a,min(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,quantile(mylm$resid,0.25))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'median',header=TRUE)
a<-table.element(a,median(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
a<-table.element(a,mean(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,quantile(mylm$resid,0.75))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'maximum',header=TRUE)
a<-table.element(a,max(mylm$resid))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')