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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 11:34:52 -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/t1257359761ncevwf60ei2sua2.htm/, Retrieved Mon, 29 Apr 2024 10:52:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53785, Retrieved Mon, 29 Apr 2024 10:52:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact152
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Explorative Data Analysis] [Workshop 5] [2009-11-04 18:34:52] [ee8fc1691ecec7724e0ca78f0c288737] [Current]
- RMPD    [Pearson Correlation] [SHWWS5review2] [2009-11-06 09:46:42] [a66d3a79ef9e5308cd94a469bc5ca464]
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Dataseries X:
-203.7281445
-196.9726865
-200.1506505
-210.2286145
-227.7644485
-237.2983405
-253.9913085
-241.3963985
-228.0971345
-237.3109325
-224.9140805
-268.2554745
-261.5617705
-255.7862185
-275.3728905
-311.2430865
-341.7372605
-359.0081925
-367.5345825
-435.1730325
-538.9686165
-461.3408165
-496.8153665
-527.5102765
-521.4649985
-531.7308405
-522.8233385
-535.0308405
-504.9279585
-506.8039805
-548.4584365
-603.5787345
-621.9315145
-574.0368705
-575.6959465
-540.1453745
-530.8674105
-544.6785305
-490.7492585
-456.9609105
-496.5877705
-545.7175125
-595.7901205
-583.8247485
-549.3199245
-535.2332525
-538.5218665
-493.5669405
-447.9817405
-290.5399385
-222.1932505
-176.5930465
-193.1584185
-186.7603605
-201.9533285
-243.8499145
-265.3850125
-299.7941905
-315.8387325
-391.1181065
-392.8294925
Dataseries Y:
-168.2281445
-192.0726865
-201.9506505
-185.3286145
-213.8644485
-230.8983405
-216.2913085
-206.3963985
-192.8971345
-205.3109325
-211.4140805
-227.6554745
-206.6617705
-222.6862185
-248.7728905
-274.7430865
-326.1372605
-348.8081925
-338.4345825
-409.9730325
-493.5686165
-437.6408165
-462.6153665
-473.8102765
-429.9649985
-475.4308405
-454.0233385
-432.7308405
-466.4279585
-467.8039805
-484.7584365
-541.9787345
-548.9315145
-533.1368705
-523.8959465
-472.0453745
-447.3674105
-490.4785305
-432.1492585
-379.1609105
-430.2877705
-540.0175125
-537.7901205
-533.7247485
-511.1199245
-524.0332525
-504.8218665
-430.8669405
-381.5817405
-223.7399385
-166.8932505
-86.49304654
-51.05841854
-143.5603605
-117.5533285
-151.8499145
-211.0850125
-272.8941905
-295.3387325
-365.2181065
-351.6294925




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53785&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53785&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53785&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'George Udny Yule' @ 72.249.76.132







Model: Y[t] = c + b X[t] + e[t]
c32.6643813230883
b0.964990741463288

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 32.6643813230883 \tabularnewline
b & 0.964990741463288 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53785&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]32.6643813230883[/C][/ROW]
[ROW][C]b[/C][C]0.964990741463288[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53785&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53785&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]
c32.6643813230883
b0.964990741463288







Descriptive Statistics about e[t]
# observations61
minimum-46.069546806212
Q1-16.2599536461069
median-4.29675260509245
mean-1.50055002576482e-15
Q313.0785350301572
maximum102.673285625103

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 61 \tabularnewline
minimum & -46.069546806212 \tabularnewline
Q1 & -16.2599536461069 \tabularnewline
median & -4.29675260509245 \tabularnewline
mean & -1.50055002576482e-15 \tabularnewline
Q3 & 13.0785350301572 \tabularnewline
maximum & 102.673285625103 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53785&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]61[/C][/ROW]
[ROW][C]minimum[/C][C]-46.069546806212[/C][/ROW]
[ROW][C]Q1[/C][C]-16.2599536461069[/C][/ROW]
[ROW][C]median[/C][C]-4.29675260509245[/C][/ROW]
[ROW][C]mean[/C][C]-1.50055002576482e-15[/C][/ROW]
[ROW][C]Q3[/C][C]13.0785350301572[/C][/ROW]
[ROW][C]maximum[/C][C]102.673285625103[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53785&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53785&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]
# observations61
minimum-46.069546806212
Q1-16.2599536461069
median-4.29675260509245
mean-1.50055002576482e-15
Q313.0785350301572
maximum102.673285625103



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')