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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 computationWed, 28 Oct 2009 10:24:24 -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/28/t1256747127oga95djpc2bzn4v.htm/, Retrieved Mon, 06 May 2024 05:29:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51516, Retrieved Mon, 06 May 2024 05:29:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWS5 bivariate eda e'(t) en e(t)
Estimated Impact107

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] [] [2009-10-28 16:24:24] [4563e36d4b7005634fe3557528d9fcab] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
590
67
-57
-18,4
-527,8
-309,4
-744,6
-844,2
231,4
-253
-493,6
84,4
-194
167,4
408,2
-253
-864,8
-112,8
-193,8
-906,2
-1208
-584,2
-298,2
-425,2
-16,8
-437,8
-450,4
-747
973,8
796,8
321,6
264,8
1139,6
904,4
1049,8
1055
1245,8
1171,4
1210
594,8
-353
150,2
-396,6
-903
103,2
331,8
191,4
371
1065
543,6
789
334,6
512,6
-216
148,4
-344,8
38,4
669,8
316,2
-150
897,6
993,4
259
411,4
393,4
-287,4
-121
-447
-34,2
303,6
150,4
-169,8
Dataseries Y:
Download CSV
Histogram 
646
-396
47
-953,4
-1263,8
-1166,4
-1896,6
-1689,2
181,4
-1132
-1065,6
-560,6
-131
45,4
341,2
-1211
-1952,8
-721,8
-1442,8
-2124,2
-1727
-1242,2
-811,2
-1969,2
87,2
-1144,8
-1084,4
-1526
1291,8
344,8
64,6
-75,2
2006,6
1654,4
1494,8
660
2173,8
1859,4
1829
514,8
-879
23,2
-753,6
-986
734,2
1162,8
1028,4
415
2304
1123,6
1489
-323,4
-958,4
-1077
-1397,6
-826,8
85,4
937,8
948,2
-333
1757,6
963,4
-371
-538,6
-1033,6
-1959,4
-1118
-1742
-280,2
282,6
463,4
-435,8

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51516&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]5 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=51516&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51516&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 time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Model: Y[t] = c + b X[t] + e[t]
c-376.373273589167
b1.74448165361852

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -376.373273589167 \tabularnewline
b & 1.74448165361852 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51516&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-376.373273589167[/C][/ROW]
[ROW][C]b[/C][C]1.74448165361852[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51516&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51516&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-376.373273589167
b1.74448165361852






Descriptive Statistics about e[t]
# observations72
minimum-1476.24802205569
Q1-316.659323341318
median89.9640877044815
mean1.80781315843130e-14
Q3330.200530438508
maximum1070.87948508658

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 72 \tabularnewline
minimum & -1476.24802205569 \tabularnewline
Q1 & -316.659323341318 \tabularnewline
median & 89.9640877044815 \tabularnewline
mean & 1.80781315843130e-14 \tabularnewline
Q3 & 330.200530438508 \tabularnewline
maximum & 1070.87948508658 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51516&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]72[/C][/ROW]
[ROW][C]minimum[/C][C]-1476.24802205569[/C][/ROW]
[ROW][C]Q1[/C][C]-316.659323341318[/C][/ROW]
[ROW][C]median[/C][C]89.9640877044815[/C][/ROW]
[ROW][C]mean[/C][C]1.80781315843130e-14[/C][/ROW]
[ROW][C]Q3[/C][C]330.200530438508[/C][/ROW]
[ROW][C]maximum[/C][C]1070.87948508658[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51516&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51516&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]
# observations72
minimum-1476.24802205569
Q1-316.659323341318
median89.9640877044815
mean1.80781315843130e-14
Q3330.200530438508
maximum1070.87948508658


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')