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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, 04 Nov 2009 10:26:42 -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/t1257355668a12xrut6m15wz02.htm/, Retrieved Mon, 29 Apr 2024 11:40:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53747, Retrieved Mon, 29 Apr 2024 11:40:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsshwws5vr1opl7
Estimated Impact144

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] [] [2009-11-04 17:26:42] [4407d6264e55b051ec65750e6dca2820] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
735,07
-1226,348
811,434
824,888
421,362
-513,422
-190,064
-196,224
548,168
603,85
304,79
120,216
602,984
-1070,538
895,432
615,108
140,41
-1028,07
-762,444
-245,956
-119,224
-216,468
655,79
785,424
761,536
-1075,126
798,71
649,59
547,884
-485,194
185,812
317,828
1063,094
339,618
288,61
1020,778
394,24
-273,966
573,402
556,436
-219,1
-1589,666
-868,892
508,342
-407,442
2,922
160,734
249,968
116,214
-1918,548
-262,954
-457,512
-1736,924
-1853,188
-1579,46
-752,802
-598,48
-292,484
-232,932
-245,918
494,592
Dataseries Y:
Download CSV
Histogram 
-3,041
-30,8449
-73,4388
-27,0621
-0,7944
-67,9576
-17,8417
-64,8297
-65,2931
-13,417
22,335
4,5873
15,8937
37,4506
-31,5559
19,8439
21,801
-10,213
35,3443
-31,1783
-6,1197
35,5541
-1,42
25,7907
11,8533
-5,8368
-34,609
15,54
18,5837
27,0118
-6,8619
-4,8801
26,2342
70,4244
27,461
29,7524
34,0675
38,1812
-10,1124
2,7433
31,2555
-0,2038
18,2439
10,9496
-13,5786
9,2486
111,4112
16,1069
36,6952
54,7651
-49,9012
26,4429
28,9053
14,0231
36,6025
-56,4916
11,3715
65,1273
192,7919
87,0766
136,3671

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53747&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]6 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=53747&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53747&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 time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Model: Y[t] = c + b X[t] + e[t]
c12.5517300624438
b-0.00442207574085306

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 12.5517300624438 \tabularnewline
b & -0.00442207574085306 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53747&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]12.5517300624438[/C][/ROW]
[ROW][C]b[/C][C]-0.00442207574085306[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53747&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53747&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]
c12.5517300624438
b-0.00442207574085306






Descriptive Statistics about e[t]
# observations61
minimum-82.7797210334641
Q1-23.1428186654042
median1.84986370293489
mean-4.47729285340637e-16
Q317.7348931427353
maximum179.210126991088

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 61 \tabularnewline
minimum & -82.7797210334641 \tabularnewline
Q1 & -23.1428186654042 \tabularnewline
median & 1.84986370293489 \tabularnewline
mean & -4.47729285340637e-16 \tabularnewline
Q3 & 17.7348931427353 \tabularnewline
maximum & 179.210126991088 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53747&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]-82.7797210334641[/C][/ROW]
[ROW][C]Q1[/C][C]-23.1428186654042[/C][/ROW]
[ROW][C]median[/C][C]1.84986370293489[/C][/ROW]
[ROW][C]mean[/C][C]-4.47729285340637e-16[/C][/ROW]
[ROW][C]Q3[/C][C]17.7348931427353[/C][/ROW]
[ROW][C]maximum[/C][C]179.210126991088[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53747&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53747&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]
# observations61
minimum-82.7797210334641
Q1-23.1428186654042
median1.84986370293489
mean-4.47729285340637e-16
Q317.7348931427353
maximum179.210126991088


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')