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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 computationMon, 02 Nov 2009 09:18:36 -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/02/t12571788042yk1t4osyg5xln6.htm/, Retrieved Sat, 04 May 2024 02:24:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=52753, Retrieved Sat, 04 May 2024 02:24:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact152
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [WorkShop2 (SHW)] [2009-10-11 20:46:43] [37daf76adc256428993ec4063536c760]
-    D  [Univariate Data Series] [WorkShop3 (SHW)] [2009-10-20 22:45:10] [37daf76adc256428993ec4063536c760]
-         [Univariate Data Series] [workShop4 (SHW)] [2009-10-28 21:56:11] [74be16979710d4c4e7c6647856088456]
- RMPD      [Bivariate Explorative Data Analysis] [WorkShop4 (SHW)] [2009-10-28 22:45:56] [74be16979710d4c4e7c6647856088456]
-  M D          [Bivariate Explorative Data Analysis] [ws4 part2] [2009-11-02 16:18:36] [95523ebdb89b97dbf680ec91e0b4bca2] [Current]
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Dataseries X:
581
597
587
536
524
537
536
533
528
516
502
506
518
534
528
478
469
490
493
508
517
514
510
527
542
565
555
499
511
526
532
549
561
557
566
588
620
626
620
573
573
574
580
590
593
597
595
612
628
629
621
569
567
573
584
589
591
595
594
611
Dataseries Y:
286.525
282.965
276.61
277.838
277.051
277.026
274.96
270.073
267.063
264.916
287.182
291.109
292.223
288.109
281.4
282.579
280.113
280.331
276.759
275.139
274.275
271.234
289.725
290.649
292.223
278.429
269.749
265.784
268.957
264.099
255.121
253.276
245.98
235.295
258.479
260.916
254.586
250.566
243.345
247.028
248.464
244.962
237.003
237.008
225.477
226.762
247.857
248.256
246.892
245.021
246.186
255.688
264.242
268.27
272.969
273.886
267.353
271.916
292.633
295.804




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52753&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52753&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52753&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Model: Y[t] = c + b X[t] + e[t]
c389.578617471069
b-0.221349361185499

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 389.578617471069 \tabularnewline
b & -0.221349361185499 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52753&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]389.578617471069[/C][/ROW]
[ROW][C]b[/C][C]-0.221349361185499[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52753&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52753&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]
c389.578617471069
b-0.221349361185499







Descriptive Statistics about e[t]
# observations60
minimum-32.8414462880677
Q1-9.01022352391847
median-1.03031028128291
mean-7.57611566074938e-16
Q39.7051737483546
maximum41.4698422132712

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -32.8414462880677 \tabularnewline
Q1 & -9.01022352391847 \tabularnewline
median & -1.03031028128291 \tabularnewline
mean & -7.57611566074938e-16 \tabularnewline
Q3 & 9.7051737483546 \tabularnewline
maximum & 41.4698422132712 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52753&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]-32.8414462880677[/C][/ROW]
[ROW][C]Q1[/C][C]-9.01022352391847[/C][/ROW]
[ROW][C]median[/C][C]-1.03031028128291[/C][/ROW]
[ROW][C]mean[/C][C]-7.57611566074938e-16[/C][/ROW]
[ROW][C]Q3[/C][C]9.7051737483546[/C][/ROW]
[ROW][C]maximum[/C][C]41.4698422132712[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52753&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52753&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-32.8414462880677
Q1-9.01022352391847
median-1.03031028128291
mean-7.57611566074938e-16
Q39.7051737483546
maximum41.4698422132712



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')