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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 computationWed, 04 Nov 2009 08:53:11 -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/04/t1257350138u7969ey5j0ep9cq.htm/, Retrieved Mon, 29 Apr 2024 12:43:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53677, Retrieved Mon, 29 Apr 2024 12:43:02 +0000
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Original text written by user:Xt uitgezuiverd door Zt WS5 Bivariate eda
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact172
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Explorative Data Analysis] [Xt uitgezuiverd d...] [2009-11-04 15:53:11] [85defb7a20869746625978e6577e6e44] [Current]
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Dataseries X:
29211
29870
32045
32209
32062
31623
32652
30030
29220
29894
28375
29082
29699
27736
28417
28412
29158
29099
29981
30560
31645
31761
32543
33313
34318
36850
35171
35317
36010
36216
37668
38994
38723
38981
39375
39958
39464
40061
40216
39824
40682
41632
41340
41893
41454
42224
42581
44372
43560
44959
45354
45173
46021
44923
44731
46597
44071
45190
43860
44595
44112
45170
44002
43981
44465
42478
41200
41232
Dataseries Y:
45319
46603
47943
45773
53225
57281
62241
61814
70727
67625
65618
63052
62410
60571
62543
62985
62739
63552
64171
63063
62985
63005
61880
62555
62254
63290
65570
66277
67061
66779
67350
67746
64551
65550
64260
61208
59603
56345
53474
50877
51749
51580
51039
50708
51245
49937
50274
51134
52514
54027
57526
59303
60939
60808
60109
59678
60092
60292
60927
59801
58352
59298
59234
56696
55434
56458
56951
56756




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=53677&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=53677&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53677&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Model: Y[t] = c + b X[t] + e[t]
c69133.7452722047
b-0.268360971346096

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 69133.7452722047 \tabularnewline
b & -0.268360971346096 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53677&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]69133.7452722047[/C][/ROW]
[ROW][C]b[/C][C]-0.268360971346096[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53677&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53677&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]
c69133.7452722047
b-0.268360971346096







Descriptive Statistics about e[t]
# observations68
minimum-15975.6529382139
Q1-3122.71983992344
median1601.12715739690
mean1.53952014535301e-13
Q33133.61051302332
maximum9434.76231052824

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 68 \tabularnewline
minimum & -15975.6529382139 \tabularnewline
Q1 & -3122.71983992344 \tabularnewline
median & 1601.12715739690 \tabularnewline
mean & 1.53952014535301e-13 \tabularnewline
Q3 & 3133.61051302332 \tabularnewline
maximum & 9434.76231052824 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53677&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]68[/C][/ROW]
[ROW][C]minimum[/C][C]-15975.6529382139[/C][/ROW]
[ROW][C]Q1[/C][C]-3122.71983992344[/C][/ROW]
[ROW][C]median[/C][C]1601.12715739690[/C][/ROW]
[ROW][C]mean[/C][C]1.53952014535301e-13[/C][/ROW]
[ROW][C]Q3[/C][C]3133.61051302332[/C][/ROW]
[ROW][C]maximum[/C][C]9434.76231052824[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53677&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53677&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]
# observations68
minimum-15975.6529382139
Q1-3122.71983992344
median1601.12715739690
mean1.53952014535301e-13
Q33133.61051302332
maximum9434.76231052824



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')