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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 11:48:53 -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/t1257360625h17xgddj93x977z.htm/, Retrieved Mon, 29 Apr 2024 13:37:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53798, Retrieved Mon, 29 Apr 2024 13:37:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsRob_WS5
Estimated Impact197

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] [2009-10-28 17:21:02] [03557919bc1ce1475f4920f6a43c36b0]
-    D  [Bivariate Explorative Data Analysis] [SHW_W5.3] [2009-10-30 14:41:18] [f966872135bb25240f339c0c372beeec]
-  M D      [Bivariate Explorative Data Analysis] [] [2009-11-04 18:48:53] [9002751dd674b8c934bf183fdf4510e9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100.3
101.9
102.1
103.2
103.7
106.2
107.7
109.9
111.7
114.9
116.0
118.3
120.4
126.0
128.1
130.1
130.8
133.6
134.2
135.5
136.2
139.1
139.0
139.6
138.7
140.9
141.3
141.8
142.0
144.5
144.6
145.5
146.8
149.5
149.9
150.1
150.9
152.8
153.1
154.0
154.9
156.9
158.4
159.7
160.2
163.2
163.7
164.4
163.7
165.5
165.6
166.8
167.5
170.6
170.9
172.0
171.8
173.9
174.0
173.8
173.9
176.0
176.6
178.2
179.2
181.3
181.8
182.9
183.8
186.3
187.4
189.2
189.7
191.9
192.6
193.7
194.2
197.6
199.3
201.4
203.0
206.3
207.1
209.8
211.1
215.3
217.4
215.5
210.9
212.6
Dataseries Y:
Download CSV
Histogram 
1844
1790
1707
1617
1530
1453
1386
1329
1255
1201
1179
1167
1160
1161
1203
1290
1404
1550
1670
1746
1817
1836
1872
1956
2010
1933
1917
1900
1829
1805
1730
1650
1618
1582
1576
1512
1540
1519
1473
1402
1299
1267
1200
1145
1103
1076
1095
1080
1082
1060
1027
1003
987
959
913
912
889
882
901
913
921
914
950
894
930
890
898
880
840
845
819
838
833
837
850
927
932
978
978
974
977
950
935
913
942
990
1075
1181
1336
1492

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53798&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]3 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=53798&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53798&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 time3 seconds
R Server'George Udny Yule' @ 72.249.76.132






Model: Y[t] = c + b X[t] + e[t]
c2413.37901625592
b-7.26282861033398

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 2413.37901625592 \tabularnewline
b & -7.26282861033398 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53798&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]2413.37901625592[/C][/ROW]
[ROW][C]b[/C][C]-7.26282861033398[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53798&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53798&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]
c2413.37901625592
b-7.26282861033398






Descriptive Statistics about e[t]
# observations90
minimum-391.890897457178
Q1-209.048465319326
median-75.2534072816442
mean-2.3125945602942e-14
Q3208.728786091137
maximum622.698346301085

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 90 \tabularnewline
minimum & -391.890897457178 \tabularnewline
Q1 & -209.048465319326 \tabularnewline
median & -75.2534072816442 \tabularnewline
mean & -2.3125945602942e-14 \tabularnewline
Q3 & 208.728786091137 \tabularnewline
maximum & 622.698346301085 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53798&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]90[/C][/ROW]
[ROW][C]minimum[/C][C]-391.890897457178[/C][/ROW]
[ROW][C]Q1[/C][C]-209.048465319326[/C][/ROW]
[ROW][C]median[/C][C]-75.2534072816442[/C][/ROW]
[ROW][C]mean[/C][C]-2.3125945602942e-14[/C][/ROW]
[ROW][C]Q3[/C][C]208.728786091137[/C][/ROW]
[ROW][C]maximum[/C][C]622.698346301085[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53798&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53798&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]
# observations90
minimum-391.890897457178
Q1-209.048465319326
median-75.2534072816442
mean-2.3125945602942e-14
Q3208.728786091137
maximum622.698346301085


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')