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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 computationSat, 07 Nov 2009 09:00:03 -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/07/t1257609666y94kuqhpsolgmaa.htm/, Retrieved Mon, 06 May 2024 13:51:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=54444, Retrieved Mon, 06 May 2024 13:51:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Explorative Data Analysis] [workshop] [2009-11-03 21:30:48] [28d531aeb5ea2ff1b676cbab66947a19]
- R  D    [Bivariate Explorative Data Analysis] [ws5] [2009-11-07 16:00:03] [8534694572259a751b15088328cf5047] [Current]
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Dataseries X:
283.042
276.687
277.915
277.128
277.103
275.037
270.150
267.140
264.993
287.259
291.186
292.300
288.186
281.477
282.656
280.190
280.408
276.836
275.216
274.352
271.311
289.802
290.726
292.300
278.506
269.826
265.861
269.034
264.176
255.198
253.353
246.057
235.372
258.556
260.993
254.663
250.643
243.422
247.105
248.541
245.039
237.080
237.085
225.554
226.839
247.934
248.333
246.969
245.098
246.263
255.765
264.319
268.347
273.046
273.963
267.430
271.993
292.710
295.881
293.299
Dataseries Y:
111.632
106.707
108.827
108.413
106.249
104.861
102.382
100.320
100.228
117.089
121.523
114.948
112.831
107.605
108.928
101.993
102.850
99.925
101.536
99.450
98.305
110.159
109.483
106.810
96.279
91.982
90.276
90.999
86.622
83.117
80.367
77.550
77.443
92.844
92.175
84.822
81.632
78.872
81.485
80.651
78.192
76.844
76.335
71.415
73.889
86.822
86.371
83.469
82.662
82.880
89.406
95.378
97.657
100.247
99.180
97.493
101.628
114.585
115.669
111.311




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=54444&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=54444&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54444&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]
c-86.1979092118182
b0.683244598360315

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -86.1979092118182 \tabularnewline
b & 0.683244598360315 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54444&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-86.1979092118182[/C][/ROW]
[ROW][C]b[/C][C]0.683244598360315[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54444&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54444&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]
c-86.1979092118182
b0.683244598360315







Descriptive Statistics about e[t]
# observations60
minimum-7.8108108991198
Q1-2.95839154562539
median0.668123217666539
mean-5.73506809172416e-17
Q32.84237829671702
maximum8.7696475936714

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -7.8108108991198 \tabularnewline
Q1 & -2.95839154562539 \tabularnewline
median & 0.668123217666539 \tabularnewline
mean & -5.73506809172416e-17 \tabularnewline
Q3 & 2.84237829671702 \tabularnewline
maximum & 8.7696475936714 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54444&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]-7.8108108991198[/C][/ROW]
[ROW][C]Q1[/C][C]-2.95839154562539[/C][/ROW]
[ROW][C]median[/C][C]0.668123217666539[/C][/ROW]
[ROW][C]mean[/C][C]-5.73506809172416e-17[/C][/ROW]
[ROW][C]Q3[/C][C]2.84237829671702[/C][/ROW]
[ROW][C]maximum[/C][C]8.7696475936714[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54444&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54444&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-7.8108108991198
Q1-2.95839154562539
median0.668123217666539
mean-5.73506809172416e-17
Q32.84237829671702
maximum8.7696475936714



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')