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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 12:11:46 -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/t1256753571isjg0lykqbfbg5o.htm/, Retrieved Mon, 06 May 2024 05:21:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51686, Retrieved Mon, 06 May 2024 05:21:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsRob_WS4_P2
Estimated Impact108

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [WS2] [2009-10-12 17:24:11] [4f76e114ed5e444b1133aad392380aad]
- RMPD    [Bivariate Explorative Data Analysis] [] [2009-10-28 18:11:46] [9002751dd674b8c934bf183fdf4510e9] [Current]
-  M D      [Bivariate Explorative Data Analysis] [ln op reeksen toe...] [2009-11-02 16:30:30] [cd6314e7e707a6546bd4604c9d1f2b69]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
106370
109375
116476
123297
114813
117925
126466
131235
120546
123791
129813
133463
122987
125418
130199
133016
121454
122044
128313
131556
120027
123001
130111
132524
123742
124931
133646
136557
127509
128945
137191
139716
129083
131604
139413
143125
133948
137116
144864
149277
138796
143258
150034
154708
144888
148762
156500
161088
152772
158011
163318
169969
162269
165765
170600
174681
166364
170240
176150
182056
172218
177856
182253
188090
176863
183273
187969
194650
183036
189516
193805
200499
188142
193732
197126
205140
191751
196700
199784
207360
196101
200824
205743
212489
200810
203683
207286
210910
194915
217920
Dataseries Y:
Download CSV
Histogram 
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

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=51686&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=51686&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51686&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]
c7.83633904011585
b0.000963927468286331

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 7.83633904011585 \tabularnewline
b & 0.000963927468286331 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51686&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]7.83633904011585[/C][/ROW]
[ROW][C]b[/C][C]0.000963927468286331[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51686&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51686&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]
c7.83633904011585
b0.000963927468286331






Descriptive Statistics about e[t]
# observations90
minimum-24.4373603406725
Q1-5.78943960353614
median1.11442240041259
mean-2.64251390830811e-16
Q35.92902894391859
maximum15.1797384788539

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 90 \tabularnewline
minimum & -24.4373603406725 \tabularnewline
Q1 & -5.78943960353614 \tabularnewline
median & 1.11442240041259 \tabularnewline
mean & -2.64251390830811e-16 \tabularnewline
Q3 & 5.92902894391859 \tabularnewline
maximum & 15.1797384788539 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51686&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]-24.4373603406725[/C][/ROW]
[ROW][C]Q1[/C][C]-5.78943960353614[/C][/ROW]
[ROW][C]median[/C][C]1.11442240041259[/C][/ROW]
[ROW][C]mean[/C][C]-2.64251390830811e-16[/C][/ROW]
[ROW][C]Q3[/C][C]5.92902894391859[/C][/ROW]
[ROW][C]maximum[/C][C]15.1797384788539[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51686&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51686&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-24.4373603406725
Q1-5.78943960353614
median1.11442240041259
mean-2.64251390830811e-16
Q35.92902894391859
maximum15.1797384788539


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')