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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 08:49:53 -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/t1257349892efzjcipuip5tast.htm/, Retrieved Mon, 29 Apr 2024 14:27:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53670, Retrieved Mon, 29 Apr 2024 14:27:57 +0000
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Original text written by user:populatieaangroei gezuiverd met het sterftecijfer in bivariate EDA
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact125
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Explorative Data Analysis] [Yt gezuiverd van ...] [2009-11-04 15:49:53] [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:
18234
18989
18315
16020
21277
25816
29747
32185
39228
37028
34974
31455
30342
31150
32564
33756
32960
34947
30217
32096
30015
29706
27064
30058
28177
27062
28541
29117
30572
32482
31412
31029
25084
29468
30462
24755
24535
19576
18101
16006
14926
12766
13440
14723
15448
11842
11492
13342
16334
22768
22397
12430
16714
23804
25533
25648
23595
21547
22757
24885
27730
33168
24939
20630
28186
25205
28906
33856




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53670&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]6 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=53670&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53670&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 time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Model: Y[t] = c + b X[t] + e[t]
c47794.5860953127
b-0.600759250186705

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 47794.5860953127 \tabularnewline
b & -0.600759250186705 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53670&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]47794.5860953127[/C][/ROW]
[ROW][C]b[/C][C]-0.600759250186705[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53670&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53670&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]
c47794.5860953127
b-0.600759250186705







Descriptive Statistics about e[t]
# observations68
minimum-12424.7314060492
Q1-4436.55561948232
median1358.55344597175
mean8.54741114487885e-14
Q33967.44390725629
maximum12509.7092356207

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 68 \tabularnewline
minimum & -12424.7314060492 \tabularnewline
Q1 & -4436.55561948232 \tabularnewline
median & 1358.55344597175 \tabularnewline
mean & 8.54741114487885e-14 \tabularnewline
Q3 & 3967.44390725629 \tabularnewline
maximum & 12509.7092356207 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53670&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]-12424.7314060492[/C][/ROW]
[ROW][C]Q1[/C][C]-4436.55561948232[/C][/ROW]
[ROW][C]median[/C][C]1358.55344597175[/C][/ROW]
[ROW][C]mean[/C][C]8.54741114487885e-14[/C][/ROW]
[ROW][C]Q3[/C][C]3967.44390725629[/C][/ROW]
[ROW][C]maximum[/C][C]12509.7092356207[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53670&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53670&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-12424.7314060492
Q1-4436.55561948232
median1358.55344597175
mean8.54741114487885e-14
Q33967.44390725629
maximum12509.7092356207



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')