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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 11:21:02 -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/t1256750783n375sidhei23aet.htm/, Retrieved Mon, 06 May 2024 05:05:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51622, Retrieved Mon, 06 May 2024 05:05:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWSH 5 Bivariate Y en Z
Estimated Impact164

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] [e7a989b306049c061a54f626f1127c12] [Current]
-    D    [Bivariate Explorative Data Analysis] [SHW_W5.3] [2009-10-30 14:41:18] [f966872135bb25240f339c0c372beeec]
-  M D      [Bivariate Explorative Data Analysis] [] [2009-11-04 18:42:24] [4f76e114ed5e444b1133aad392380aad]
-  M D      [Bivariate Explorative Data Analysis] [] [2009-11-04 18:48:53] [4f76e114ed5e444b1133aad392380aad]
-  M D      [Bivariate Explorative Data Analysis] [] [2009-11-04 18:52:50] [4f76e114ed5e444b1133aad392380aad]
-  M D      [Bivariate Explorative Data Analysis] [] [2009-11-04 19:12:31] [4f76e114ed5e444b1133aad392380aad]
- R  D    [Bivariate Explorative Data Analysis] [SHWW5-3] [2009-10-30 14:42:27] [ff6896cd60d3b2257a9a5027c462fa18]
-    D      [Bivariate Explorative Data Analysis] [SHWW5-4] [2009-10-30 15:00:17] [ff6896cd60d3b2257a9a5027c462fa18]
-    D      [Bivariate Explorative Data Analysis] [SHWW5-5] [2009-10-30 15:16:30] [ff6896cd60d3b2257a9a5027c462fa18]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
125.9
133.1
147
145.8
164.4
149.8
137.7
151.7
156.8
180
180.4
170.4
191.6
199.5
218.2
217.5
205
194
199.3
219.3
211.1
215.2
240.2
242.2
240.7
255.4
253
218.2
203.7
205.6
215.6
188.5
202.9
214
230.3
230
241
259.6
247.8
270.3
289.7
322.7
315
320.2
329.5
360.6
382.2
435.4
464
468.8
403
351.6
252
188
146.5
152.9
148.1
165.1
177
206.1
Dataseries Y:
Download CSV
Histogram 
130.7
117.2
110.8
111.4
108.2
108.8
110.2
109.5
109.5
116
111.2
112.1
114
119.1
114.1
115.1
115.4
110.8
116
119.2
126.5
127.8
131.3
140.3
137.3
143
134.5
139.9
159.3
170.4
175
175.8
180.9
180.3
169.6
172.3
184.8
177.7
184.6
211.4
215.3
215.9
244.7
259.3
289
310.9
321
315.1
333.2
314.1
284.7
273.9
216
196.4
190.9
206.4
196.3
199.5
198.9
214.4

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=51622&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=51622&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51622&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]
c20.9556337712912
b0.66235364764947

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 20.9556337712912 \tabularnewline
b & 0.66235364764947 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51622&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]20.9556337712912[/C][/ROW]
[ROW][C]b[/C][C]0.66235364764947[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51622&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51622&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]
c20.9556337712912
b0.66235364764947






Descriptive Statistics about e[t]
# observations60
minimum-54.0311066266072
Q1-30.3988220206742
median-2.57294241332555
mean-2.90970951037176e-15
Q321.433820566524
maximum84.1704935031048

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -54.0311066266072 \tabularnewline
Q1 & -30.3988220206742 \tabularnewline
median & -2.57294241332555 \tabularnewline
mean & -2.90970951037176e-15 \tabularnewline
Q3 & 21.433820566524 \tabularnewline
maximum & 84.1704935031048 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51622&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]-54.0311066266072[/C][/ROW]
[ROW][C]Q1[/C][C]-30.3988220206742[/C][/ROW]
[ROW][C]median[/C][C]-2.57294241332555[/C][/ROW]
[ROW][C]mean[/C][C]-2.90970951037176e-15[/C][/ROW]
[ROW][C]Q3[/C][C]21.433820566524[/C][/ROW]
[ROW][C]maximum[/C][C]84.1704935031048[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51622&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51622&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]
# observations60
minimum-54.0311066266072
Q1-30.3988220206742
median-2.57294241332555
mean-2.90970951037176e-15
Q321.433820566524
maximum84.1704935031048


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')