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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 computationMon, 02 Nov 2009 10:26:06 -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/02/t1257183051bj8edga0njgrru7.htm/, Retrieved Fri, 03 May 2024 16:43:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=52830, Retrieved Fri, 03 May 2024 16:43:09 +0000
QR Codes:

Original text written by user:Y = populatieaangroei jaarlijks x= geboortecijfer jaarlijks
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact107
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]
- RMPD    [Bivariate Explorative Data Analysis] [Bivariate EDA WS4...] [2009-11-02 17:26:06] [85defb7a20869746625978e6577e6e44] [Current]
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Dataseries X:
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
40915
56037
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
58545
58459




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=52830&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=52830&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52830&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 time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Model: Y[t] = c + b X[t] + e[t]
c46023.2506750997
b0.502689860862158

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 46023.2506750997 \tabularnewline
b & 0.502689860862158 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52830&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]46023.2506750997[/C][/ROW]
[ROW][C]b[/C][C]0.502689860862158[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52830&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52830&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]
c46023.2506750997
b0.502689860862158







Descriptive Statistics about e[t]
# observations70
minimum-15733.4824082324
Q1-1768.15025501794
median577.62974676086
mean2.34479102800833e-14
Q32799.20536251756
maximum7031.31435438371

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 70 \tabularnewline
minimum & -15733.4824082324 \tabularnewline
Q1 & -1768.15025501794 \tabularnewline
median & 577.62974676086 \tabularnewline
mean & 2.34479102800833e-14 \tabularnewline
Q3 & 2799.20536251756 \tabularnewline
maximum & 7031.31435438371 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52830&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]70[/C][/ROW]
[ROW][C]minimum[/C][C]-15733.4824082324[/C][/ROW]
[ROW][C]Q1[/C][C]-1768.15025501794[/C][/ROW]
[ROW][C]median[/C][C]577.62974676086[/C][/ROW]
[ROW][C]mean[/C][C]2.34479102800833e-14[/C][/ROW]
[ROW][C]Q3[/C][C]2799.20536251756[/C][/ROW]
[ROW][C]maximum[/C][C]7031.31435438371[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52830&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52830&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]
# observations70
minimum-15733.4824082324
Q1-1768.15025501794
median577.62974676086
mean2.34479102800833e-14
Q32799.20536251756
maximum7031.31435438371



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')