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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, 03 Nov 2009 09:16:15 -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/03/t1257265045zqqx4txdlyjm0th.htm/, Retrieved Wed, 01 May 2024 15:34:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53218, Retrieved Wed, 01 May 2024 15:34:51 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsworkshop5biv
Estimated Impact139
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Explorative Data Analysis] [] [2009-11-03 16:16:15] [42ed2e0ab6f351a3dce7cf3f388e378d] [Current]
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Dataseries X:
210944
218238
212696
214774
212248
212055
210783
206482
206037
207351
215248
214080
213840
207623
202097
204023
204776
202436
198365
196066
195898
196621
205523
206211
200855
190133
183418
182122
181712
177421
172267
169850
166037
163600
177263
179606
169189
164017
159004
160079
160792
157135
153070
151414
146612
150703
163627
165264
155839
151095
148609
152353
154304
154504
154436
154589
151473
156009
169123
171568
162335
Dataseries Y:
173558
173820
171663
174110
174338
175440
174922
172188
171330
169560
174579
173740
173427
172952
170305
172717
173019
173690
172439
171914
171968
169500
173898
172308
171568
164939
161275
160770
162466
160185
154836
154103
150495
142707
149962
149967
144572
143819
141070
144119
145330
143279
139063
139202
133632
134476
141859
140693
138047
138346
140167
146796
152228
155410
159032
160312
157687
160141
167421
167628
164403




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53218&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53218&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53218&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 time8 seconds
R Server'George Udny Yule' @ 72.249.76.132







Model: Y[t] = c + b X[t] + e[t]
c68568.8508430034
b0.501697663423265

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 68568.8508430034 \tabularnewline
b & 0.501697663423265 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53218&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]68568.8508430034[/C][/ROW]
[ROW][C]b[/C][C]0.501697663423265[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53218&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53218&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]
c68568.8508430034
b0.501697663423265







Descriptive Statistics about e[t]
# observations61
minimum-10788.4134909858
Q1-4238.34551316983
median27.6122180340695
mean-7.9528369318067e-14
Q32286.85287705088
maximum14391.0589651809

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 61 \tabularnewline
minimum & -10788.4134909858 \tabularnewline
Q1 & -4238.34551316983 \tabularnewline
median & 27.6122180340695 \tabularnewline
mean & -7.9528369318067e-14 \tabularnewline
Q3 & 2286.85287705088 \tabularnewline
maximum & 14391.0589651809 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53218&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]61[/C][/ROW]
[ROW][C]minimum[/C][C]-10788.4134909858[/C][/ROW]
[ROW][C]Q1[/C][C]-4238.34551316983[/C][/ROW]
[ROW][C]median[/C][C]27.6122180340695[/C][/ROW]
[ROW][C]mean[/C][C]-7.9528369318067e-14[/C][/ROW]
[ROW][C]Q3[/C][C]2286.85287705088[/C][/ROW]
[ROW][C]maximum[/C][C]14391.0589651809[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53218&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53218&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]
# observations61
minimum-10788.4134909858
Q1-4238.34551316983
median27.6122180340695
mean-7.9528369318067e-14
Q32286.85287705088
maximum14391.0589651809



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')