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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 computationSun, 06 Dec 2009 09:19:18 -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/Dec/06/t12601164539atciejllw7phoh.htm/, Retrieved Mon, 06 May 2024 03:04:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64445, Retrieved Mon, 06 May 2024 03:04:57 +0000
QR Codes:

Original text written by user:Bivariate EDA X[t]: Amerikaanse inflatie Y[t]: US dollar
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact92

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] [Paper: Bivariate EDA] [2009-12-06 16:19:18] [7a39e26d7a09dd77604df90cb29f8d39] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.527
0.472
0.000
0.052
0.313
0.364
0.363
-0.155
0.052
0.568
0.668
1.378
0.252
-0.402
-0.050
0.555
0.050
0.150
0.450
0.299
0.199
0.496
0.444
-0.393
-0.444
0.198
0.494
0.133
0.388
0.484
0.278
0.369
0.165
0.155
0.087
0.414
0.360
0.975
0.270
0.359
0.169
0.381
0.154
0.486
0.925
0.728
-0.014
0.046
-0.819
-1.674
-0.788
0.279
0.396
-0.141
-0.019
0.099
0.742
0.005
0.448
Dataseries Y:
Download CSV
Histogram 
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

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=64445&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=64445&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64445&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]
c0.778012058274589
b0.0734527913500218

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 0.778012058274589 \tabularnewline
b & 0.0734527913500218 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64445&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]0.778012058274589[/C][/ROW]
[ROW][C]b[/C][C]0.0734527913500218[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64445&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64445&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]
c0.778012058274589
b0.0734527913500218






Descriptive Statistics about e[t]
# observations59
minimum-0.161699363649197
Q1-0.0478736296643961
median-0.0105027819671454
mean-1.65774397321109e-18
Q30.0369345095185499
maximum0.156174419987258

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 59 \tabularnewline
minimum & -0.161699363649197 \tabularnewline
Q1 & -0.0478736296643961 \tabularnewline
median & -0.0105027819671454 \tabularnewline
mean & -1.65774397321109e-18 \tabularnewline
Q3 & 0.0369345095185499 \tabularnewline
maximum & 0.156174419987258 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64445&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]-0.161699363649197[/C][/ROW]
[ROW][C]Q1[/C][C]-0.0478736296643961[/C][/ROW]
[ROW][C]median[/C][C]-0.0105027819671454[/C][/ROW]
[ROW][C]mean[/C][C]-1.65774397321109e-18[/C][/ROW]
[ROW][C]Q3[/C][C]0.0369345095185499[/C][/ROW]
[ROW][C]maximum[/C][C]0.156174419987258[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64445&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64445&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-0.161699363649197
Q1-0.0478736296643961
median-0.0105027819671454
mean-1.65774397321109e-18
Q30.0369345095185499
maximum0.156174419987258


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')