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Description of Statistical Computation

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 computationFri, 30 Oct 2009 08:46:53 -0600
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/Oct/30/t1256914369valnx7zweo33ff9.htm/, Retrieved Sun, 28 Apr 2024 19:52:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=52130, Retrieved Sun, 28 Apr 2024 19:52:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact173

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Explorative Data Analysis] [Workshop 5: Bivar...] [2009-10-30 14:46:53] [a5c6be3c0aa55fdb2a703a08e16947ef] [Current]
- RMP     [Kendall tau Rank Correlation] [Rev1-BivEDA-Corre...] [2009-11-11 10:02:29] [f15cfb7053d35072d573abca87df96a0]
- RMPD    [Kendall tau Rank Correlation] [Rev1-BivEDA-Corre...] [2009-11-11 10:05:15] [f15cfb7053d35072d573abca87df96a0]
Feedback Forum

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.7461
0.7775
0.7790
0.7744
0.7905
0.7719
0.7811
0.7557
0.7637
0.7595
0.7471
0.7615
0.7487
0.7389
0.7337
0.7510
0.7382
0.7159
0.7542
0.7636
0.7433
0.7658
0.7627
0.7480
0.7692
0.7850
0.7913
0.7720
0.7880
0.8070
0.8268
0.8244
0.8487
0.8572
0.8214
0.8827
0.9216
0.8865
0.8816
0.8884
0.9466
0.9180
0.9337
0.9559
0.9626
0.9434
0.8639
0.7996
0.6680
0.6572
0.6928
0.6438
0.6454
0.6873
0.7265
0.7912
0.8114
0.8281
0.8393
Dataseries Y:
Download CSV
Histogram 
2.86
2.55
2.27
2.26
2.57
3.07
2.76
2.51
2.87
3.14
3.11
3.16
2.47
2.57
2.89
2.63
2.38
1.69
1.96
2.19
1.87
1.60
1.63
1.22
1.21
1.49
1.64
1.66
1.77
1.82
1.78
1.28
1.29
1.37
1.12
1.51
2.24
2.94
3.09
3.46
3.64
4.39
4.15
5.21
5.80
5.91
5.39
5.46
4.72
3.14
2.63
2.32
1.93
0.62
0.60
-0.37
-1.10
-1.68
-0.78

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52130&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]2 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=52130&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52130&T=0

As an alternative you can also use a QR Code:  
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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Model: Y[t] = c + b X[t] + e[t]
c-2.32088100533463
b5.90992408626969

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -2.32088100533463 \tabularnewline
b & 5.90992408626969 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52130&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-2.32088100533463[/C][/ROW]
[ROW][C]b[/C][C]5.90992408626969[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52130&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52130&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Model: Y[t] = c + b X[t] + e[t]
c-2.32088100533463
b5.90992408626969






Descriptive Statistics about e[t]
# observations59
minimum-4.25312713050531
Q1-0.806926815790118
median0.200691930879266
mean1.35653905716746e-17
Q30.832541240868625
maximum3.09305171570647

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 59 \tabularnewline
minimum & -4.25312713050531 \tabularnewline
Q1 & -0.806926815790118 \tabularnewline
median & 0.200691930879266 \tabularnewline
mean & 1.35653905716746e-17 \tabularnewline
Q3 & 0.832541240868625 \tabularnewline
maximum & 3.09305171570647 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52130&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]59[/C][/ROW]
[ROW][C]minimum[/C][C]-4.25312713050531[/C][/ROW]
[ROW][C]Q1[/C][C]-0.806926815790118[/C][/ROW]
[ROW][C]median[/C][C]0.200691930879266[/C][/ROW]
[ROW][C]mean[/C][C]1.35653905716746e-17[/C][/ROW]
[ROW][C]Q3[/C][C]0.832541240868625[/C][/ROW]
[ROW][C]maximum[/C][C]3.09305171570647[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52130&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52130&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Descriptive Statistics about e[t]
# observations59
minimum-4.25312713050531
Q1-0.806926815790118
median0.200691930879266
mean1.35653905716746e-17
Q30.832541240868625
maximum3.09305171570647


Figures (Output of Computation)

Input Parameters & R Code

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')