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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 12:18:44 -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/t1256753993xzlc4rau7mp27q9.htm/, Retrieved Mon, 06 May 2024 02:49:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51692, Retrieved Mon, 06 May 2024 02:49:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact119

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] [af2352cd9a951bedd08ebe247d0de1a2]
-    D          [Bivariate Explorative Data Analysis] [WS4- Deel 2: Vraag 2] [2009-10-28 18:18:44] [d9efc2d105d810fc0b0ac636e31105d1] [Current]
- 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 
6.44
6.55
6.72
6.52
6.49
6.67
6.02
5.86
6.73
6.59
6.55
6.46
6.54
6.46
6.64
6.45
6.58
6.75
6.04
5.91
6.71
6.53
6.40
6.52
6.61
6.54
6.53
6.39
6.59
6.59
5.96
5.80
6.56
6.57
6.49
6.48
6.46
6.47
6.58
6.48
6.45
6.59
5.97
5.84
6.67
6.75
6.48
6.44
6.53
6.42
6.57
6.57
6.44
6.82
6.27
5.88
6.82
6.72
6.56
6.75
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 time4 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 & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51692&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]4 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=51692&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51692&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 time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Model: Y[t] = c + b X[t] + e[t]
c-1345.08049382865
b261.358097918145

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -1345.08049382865 \tabularnewline
b & 261.358097918145 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51692&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-1345.08049382865[/C][/ROW]
[ROW][C]b[/C][C]261.358097918145[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51692&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51692&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]
c-1345.08049382865
b261.358097918145






Descriptive Statistics about e[t]
# observations60
minimum-55.2693714519241
Q1-17.014118863268
median-1.56687522221047
mean1.04112320616541e-16
Q316.5794397469167
maximum49.8219807146242

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -55.2693714519241 \tabularnewline
Q1 & -17.014118863268 \tabularnewline
median & -1.56687522221047 \tabularnewline
mean & 1.04112320616541e-16 \tabularnewline
Q3 & 16.5794397469167 \tabularnewline
maximum & 49.8219807146242 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51692&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]-55.2693714519241[/C][/ROW]
[ROW][C]Q1[/C][C]-17.014118863268[/C][/ROW]
[ROW][C]median[/C][C]-1.56687522221047[/C][/ROW]
[ROW][C]mean[/C][C]1.04112320616541e-16[/C][/ROW]
[ROW][C]Q3[/C][C]16.5794397469167[/C][/ROW]
[ROW][C]maximum[/C][C]49.8219807146242[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51692&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51692&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-55.2693714519241
Q1-17.014118863268
median-1.56687522221047
mean1.04112320616541e-16
Q316.5794397469167
maximum49.8219807146242


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')