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Description of Statistical Computation

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, 28 Oct 2009 11:51:17 -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/28/t1256752387qiqc0vbzhfj176w.htm/, Retrieved Sun, 05 May 2024 21:29:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51661, Retrieved Sun, 05 May 2024 21:29:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact142

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
-   PD  [Bivariate Data Series] [WS 4- gegeven model] [2009-10-28 15:05:08] [af2352cd9a951bedd08ebe247d0de1a2]
- RMP     [Bivariate Explorative Data Analysis] [WS4- Deel 1: Vraag 1] [2009-10-28 16:22:04] [af2352cd9a951bedd08ebe247d0de1a2]
-    D        [Bivariate Explorative Data Analysis] [WS4- Deel 2: Vraag 1] [2009-10-28 17:51:17] [d9efc2d105d810fc0b0ac636e31105d1] [Current]
- RMPD          [Univariate Explorative Data Analysis] [WS4- Deel 2: Vraag 1] [2009-10-28 18:02:41] [af2352cd9a951bedd08ebe247d0de1a2]
-    D          [Bivariate Explorative Data Analysis] [WS4- Deel 2: Vraag 2] [2009-10-28 18:18:44] [af2352cd9a951bedd08ebe247d0de1a2]
- RMPD            [Univariate Explorative Data Analysis] [WS4- Deel 2: Vraag 2] [2009-10-28 18:26:25] [af2352cd9a951bedd08ebe247d0de1a2]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
627
696
825
677
656
785
412
352
839
729
696
641
695
638
762
635
721
854
418
367
824
687
601
676
740
691
683
594
729
731
386
331
707
715
657
653
642
643
718
654
632
731
392
344
792
852
649
629
685
617
715
715
629
916
531
357
917
828
708
858
Dataseries Y:
Download CSV
Histogram 
356
386
444
387
327
448
225
182
460
411
342
361
377
331
428
340
352
461
221
198
422
329
320
375
364
351
380
319
322
386
221
187
344
342
365
313
356
337
389
326
343
357
220
228
391
425
332
298
360
326
325
393
301
426
265
210
429
440
357
431

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51661&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]2 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=51661&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51661&T=0

As an alternative you can also use a QR Code:  
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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Model: Y[t] = c + b X[t] + e[t]
c37.0270949563814
b0.465348987030546

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 37.0270949563814 \tabularnewline
b & 0.465348987030546 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51661&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]37.0270949563814[/C][/ROW]
[ROW][C]b[/C][C]0.465348987030546[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51661&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51661&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Model: Y[t] = c + b X[t] + e[t]
c37.0270949563814
b0.465348987030546






Descriptive Statistics about e[t]
# observations60
minimum-54.2665065016492
Q1-17.7464936170222
median0.654804255311346
mean8.41601094370148e-16
Q320.7237966686438
maximum45.6739502246403

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -54.2665065016492 \tabularnewline
Q1 & -17.7464936170222 \tabularnewline
median & 0.654804255311346 \tabularnewline
mean & 8.41601094370148e-16 \tabularnewline
Q3 & 20.7237966686438 \tabularnewline
maximum & 45.6739502246403 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51661&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]-54.2665065016492[/C][/ROW]
[ROW][C]Q1[/C][C]-17.7464936170222[/C][/ROW]
[ROW][C]median[/C][C]0.654804255311346[/C][/ROW]
[ROW][C]mean[/C][C]8.41601094370148e-16[/C][/ROW]
[ROW][C]Q3[/C][C]20.7237966686438[/C][/ROW]
[ROW][C]maximum[/C][C]45.6739502246403[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51661&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51661&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Descriptive Statistics about e[t]
# observations60
minimum-54.2665065016492
Q1-17.7464936170222
median0.654804255311346
mean8.41601094370148e-16
Q320.7237966686438
maximum45.6739502246403


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 1 ;
Parameters (R input):
par1 = 0 ; par2 = 1 ;
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')