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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 computationTue, 27 Oct 2009 10:01:09 -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/27/t1256659380tsqw1jmo8uxkqmc.htm/, Retrieved Tue, 07 May 2024 07:49:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51026, Retrieved Tue, 07 May 2024 07:49:18 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact194
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Explorative Data Analysis] [workshop 4/part 2] [2009-10-27 16:01:09] [bebfa40a4e66abcf3fcee16e050bb8d6] [Current]
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Dataseries X:
27501
26577
25705
25147
24740
24281
23577
22598
21903
21360
24226
25468
26858
25684
24938
24347
25137
24479
23810
23301
23076
23049
25870
27291
28706
28220
26612
25453
26066
25696
23851
24203
22433
19195
21955
23326
24490
23996
22181
22550
23013
22826
20544
20814
18518
17964
21139
21818
23028
21890
21833
22289
22380
22316
22614
22775
21221
20915
23889
24703
Dataseries Y:
208831401
205779025
205578244
208224900
208889209
212343184
213861376
210569121
210250000
204375616
205692964
201668401
208224900
202492900
206094736
208455844
224820036
223771681
221592996
213130801
213715161
207129664
213773641
210946576
208398096
195608196
191379556
189723076
196644529
194602500
179399236
187909264
177982281
147428164
155925169
159491641
152843769
152349649
147452449
151265401
160478224
163532944
148401124
151634596
141205689
141491025
150356644
149670756
144336196
139523344
145612489
153685609
163635264
168143089
171426649
176145984
171269569
172922500
187964100
187553025




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51026&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]3 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=51026&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51026&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Model: Y[t] = c + b X[t] + e[t]
c-1401687.25528789
b7845.3381120541

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -1401687.25528789 \tabularnewline
b & 7845.3381120541 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51026&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-1401687.25528789[/C][/ROW]
[ROW][C]b[/C][C]7845.3381120541[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51026&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51026&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]
c-1401687.25528789
b7845.3381120541







Descriptive Statistics about e[t]
# observations60
minimum-37886874.1089172
Q1-14336677.8665594
median-1760136.4831857
mean3.27937262530516e-09
Q312247290.9102019
maximum39815246.5869668

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -37886874.1089172 \tabularnewline
Q1 & -14336677.8665594 \tabularnewline
median & -1760136.4831857 \tabularnewline
mean & 3.27937262530516e-09 \tabularnewline
Q3 & 12247290.9102019 \tabularnewline
maximum & 39815246.5869668 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51026&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]-37886874.1089172[/C][/ROW]
[ROW][C]Q1[/C][C]-14336677.8665594[/C][/ROW]
[ROW][C]median[/C][C]-1760136.4831857[/C][/ROW]
[ROW][C]mean[/C][C]3.27937262530516e-09[/C][/ROW]
[ROW][C]Q3[/C][C]12247290.9102019[/C][/ROW]
[ROW][C]maximum[/C][C]39815246.5869668[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51026&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51026&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]
# observations60
minimum-37886874.1089172
Q1-14336677.8665594
median-1760136.4831857
mean3.27937262530516e-09
Q312247290.9102019
maximum39815246.5869668



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')