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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 13:51:33 -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/t1257281583pim316kj6w3zvrw.htm/, Retrieved Wed, 01 May 2024 22:17:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53412, Retrieved Wed, 01 May 2024 22:17:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact162
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Partial Correlation] [Workshop 5 correl...] [2009-10-29 15:05:45] [eaf42bcf5162b5692bb3c7f9d4636222]
- RMPD  [Bivariate Explorative Data Analysis] [workshop 5 bivari...] [2009-10-29 15:48:25] [eaf42bcf5162b5692bb3c7f9d4636222]
-  M D    [Bivariate Explorative Data Analysis] [WS5 bivariate EDA] [2009-11-02 20:57:59] [37a8d600db9abe09a2528d150ccff095]
-    D        [Bivariate Explorative Data Analysis] [WS5 E't(y) E't(x)] [2009-11-03 20:51:33] [557d56ec4b06cd0135c259898de8ce95] [Current]
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Dataseries X:
8959,322857
12764,92385
12802,46231
13571,86
14240,76818
10476,96571
12874,52667
11803,59
15271,95
12292,77
11445,59
13835,04091
9870,108571
13825,61615
10885,59
10985,8125
9666,351176
14978,13182
10149,375
15264,30846
9154,41
11204,1875
10698,76294
8304,177368
8035,097778
9298,756316
11512,0625
12362,05667
9894,125
10882,125
9980,351176
9729,44
8351,44
9487,440526
11315,82176
9965,542222
8388,651053
10709,46882
10134,14
8894,192857
6502,696667
7416,29
7519,33
7274,523077
9486,823636
7591,09
7588,884286
7050,884286
5354,743793
6648,806667
6253,633871
5205,812759
5117,769259
6323,869091
5632,13
6762,13087
5454,753846
6095,913478
7350,369091
8667,653333
Dataseries Y:
9339,18
11846,77
11878,38538
12900,52667
14390,58636
10243,18
12688,11
11353,05667
14408,58636
11750,92385
11268,52333
12738,67727
10268,82286
13005,00077
10233,72333
10856,375
10243,23353
15511,22273
10164,5625
15585,46231
9092,821765
10735,75
10203,29235
7685,23
8440,764444
8818,966842
11098,25
12002,65667
10041,625
10702,8125
9687,762941
9273,44
8091,74
9350,545789
10759,82176
9736,264444
8417,177368
10360,76294
10038,59
9116,669048
7172,363333
7921,17
7400,41
7582,907692
9751,141818
7645,53
7749,027143
7178,991429
6063,088621
6936,706667
6580,795161
5817,088621
5735,843333
6909,641818
5792,57
6949,304783
5415,484615
5989,826522
7398,414545
8162,264444




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53412&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53412&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53412&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'RServer@AstonUniversity' @ vre.aston.ac.uk







Model: Y[t] = c + b X[t] + e[t]
c852.170750747656
b0.903007941333262

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 852.170750747656 \tabularnewline
b & 0.903007941333262 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53412&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]852.170750747656[/C][/ROW]
[ROW][C]b[/C][C]0.903007941333262[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53412&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53412&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]
c852.170750747656
b0.903007941333262







Descriptive Statistics about e[t]
# observations60
minimum-665.678860291605
Q1-236.151253857401
median-33.0188696979244
mean-6.21724893790088e-15
Q3238.473837150922
maximum1133.67999945592

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -665.678860291605 \tabularnewline
Q1 & -236.151253857401 \tabularnewline
median & -33.0188696979244 \tabularnewline
mean & -6.21724893790088e-15 \tabularnewline
Q3 & 238.473837150922 \tabularnewline
maximum & 1133.67999945592 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53412&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]-665.678860291605[/C][/ROW]
[ROW][C]Q1[/C][C]-236.151253857401[/C][/ROW]
[ROW][C]median[/C][C]-33.0188696979244[/C][/ROW]
[ROW][C]mean[/C][C]-6.21724893790088e-15[/C][/ROW]
[ROW][C]Q3[/C][C]238.473837150922[/C][/ROW]
[ROW][C]maximum[/C][C]1133.67999945592[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53412&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53412&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-665.678860291605
Q1-236.151253857401
median-33.0188696979244
mean-6.21724893790088e-15
Q3238.473837150922
maximum1133.67999945592



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')