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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 computationTue, 03 Nov 2009 11:26:51 -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/t1257272878ax6y0fr8yhqikfx.htm/, Retrieved Wed, 01 May 2024 21:12:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53307, Retrieved Wed, 01 May 2024 21:12:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact126

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Explorative Data Analysis] [SHw WS5 ] [2009-11-03 18:26:51] [d9efc2d105d810fc0b0ac636e31105d1] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-39,26
-25,87
-45,916
-30,457
14,763
-54,264
-30,363
-22,122
-77,308
-38,545
10,958
-35,195
-51,587
-5,585
-65,524
-38,868
19,088
-24,895
-20,015
-27,514
-1,002
37,589
-12,128
-59,413
-1,956
-3,325
-39,348
-40,541
-14,302
-30,87
-11,015
-29,057
-4,216
14,958
-50,543
-1,019
-33,26
-22,107
-50,631
-15,65
-37,629
3,153
-48,428
-67,124
-6,805
-14,763
-12,172
9,786
-12,608
-37,65
5,937
-62,979
-3,975
40,65
-38,843
-50,558
-15,111
-39,568
-9,847
-25,285
Dataseries Y:
Download CSV
Histogram 
323,691
350,803
388,226
357,579
287,11
405,944
198,467
155,467
397,006
373,637
294,89
327,969
327,724
292,554
378,724
295,778
304,529
412,085
193,745
173,272
370,197
287,305
286,247
328,612
308,948
305,695
341,193
276,944
242,4
336,363
208,546
168,409
288,587
289,475
322,944
260,475
320,442
294,222
338,641
275,28
302,027
303,031
191,301
210,853
330,894
359,84
287,778
249,446
318,666
280,695
259,479
341,558
249,197
358,313
215,363
187,438
342,541
383,143
306,641
363,313

Tables (Output of Computation)





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=53307&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=53307&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53307&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Model: Y[t] = c + b X[t] + e[t]
c291.295443900528
b-0.328892242324538

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 291.295443900528 \tabularnewline
b & -0.328892242324538 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53307&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]291.295443900528[/C][/ROW]
[ROW][C]b[/C][C]-0.328892242324538[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53307&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53307&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]
c291.295443900528
b-0.328892242324538






Descriptive Statistics about e[t]
# observations60
minimum-143.104198085231
Q1-28.2297508869169
median12.439753764412
mean-2.34580873244757e-15
Q337.0573392089782
maximum112.601783726803

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -143.104198085231 \tabularnewline
Q1 & -28.2297508869169 \tabularnewline
median & 12.439753764412 \tabularnewline
mean & -2.34580873244757e-15 \tabularnewline
Q3 & 37.0573392089782 \tabularnewline
maximum & 112.601783726803 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53307&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]-143.104198085231[/C][/ROW]
[ROW][C]Q1[/C][C]-28.2297508869169[/C][/ROW]
[ROW][C]median[/C][C]12.439753764412[/C][/ROW]
[ROW][C]mean[/C][C]-2.34580873244757e-15[/C][/ROW]
[ROW][C]Q3[/C][C]37.0573392089782[/C][/ROW]
[ROW][C]maximum[/C][C]112.601783726803[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53307&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53307&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-143.104198085231
Q1-28.2297508869169
median12.439753764412
mean-2.34580873244757e-15
Q337.0573392089782
maximum112.601783726803


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')