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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 computationThu, 29 Oct 2009 08:37:50 -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/29/t12568271484xg6ldrxhtufj2l.htm/, Retrieved Mon, 29 Apr 2024 01:21:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51999, Retrieved Mon, 29 Apr 2024 01:21:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact175

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Trivariate Scatterplots] [trivariate analysis] [2008-11-13 08:14:40] [3b5d63cebdc58ed6c519cdb5b6a36d46]
- RMPD  [Bivariate Explorative Data Analysis] [college Y=f(Z)] [2009-10-28 12:19:32] [74be16979710d4c4e7c6647856088456]
-    D      [Bivariate Explorative Data Analysis] [WS5 berekening Y=...] [2009-10-29 14:37:50] [37de18e38c1490dd77c2b362ed87f3bb] [Current]
-  M          [Bivariate Explorative Data Analysis] [BDM2] [2009-11-03 11:53:00] [f5d341d4bbba73282fc6e80153a6d315]
-  M          [Bivariate Explorative Data Analysis] [TG2] [2009-11-03 11:59:56] [a21bac9c8d3d56fdec8be4e719e2c7ed]
-  M          [Bivariate Explorative Data Analysis] [P5] [2009-12-15 09:47:24] [f5d341d4bbba73282fc6e80153a6d315]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
84
84.5
87.3
86.3
85
86.5
85.4
81.2
81.5
82.2
86.4
86.9
88.6
91.6
89.7
85.9
89.8
91.4
93.1
95.1
94.9
101.2
105.6
112.2
119.7
128.2
129.6
129.9
121.7
125.7
130.4
128.5
130
136.7
138.1
139.5
140.4
144.6
151.4
147.9
141.5
143.8
143.6
150.5
150.1
154.9
162.1
176.7
186.6
194.8
196.3
228.8
267.2
237.2
254.7
258.2
257.9
269.6
266.9
269.6
Dataseries Y:
Download CSV
Histogram 
89.3
90.3
91.1
90.1
86.7
85.1
83.4
82
80.4
81.9
93.8
94.8
92.3
87.5
83.2
82
80.3
81.8
85.1
84.2
84.4
84.5
93.3
93.2
100.3
111.4
114.9
109.5
109.9
105.8
110.8
108.8
116.1
109.8
113.8
113.8
117.4
119.5
122.6
120.7
119
126.1
133.9
138.1
140.4
148.2
148.2
155.9
171.1
171.9
188.8
214.9
228.5
220
225.4
220.7
219.7
232.1
223.5
218.9

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51999&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]4 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=51999&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51999&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 time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Model: Y[t] = c + b X[t] + e[t]
c11.2527806979831
b0.815250803619793

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 11.2527806979831 \tabularnewline
b & 0.815250803619793 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51999&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]11.2527806979831[/C][/ROW]
[ROW][C]b[/C][C]0.815250803619793[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51999&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51999&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]
c11.2527806979831
b0.815250803619793






Descriptive Statistics about e[t]
# observations60
minimum-12.8975655528088
Q1-6.87424135828476
median-0.819166808902777
mean-4.60135402002848e-17
Q35.72062817524456
maximum17.5134865514515

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -12.8975655528088 \tabularnewline
Q1 & -6.87424135828476 \tabularnewline
median & -0.819166808902777 \tabularnewline
mean & -4.60135402002848e-17 \tabularnewline
Q3 & 5.72062817524456 \tabularnewline
maximum & 17.5134865514515 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51999&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]-12.8975655528088[/C][/ROW]
[ROW][C]Q1[/C][C]-6.87424135828476[/C][/ROW]
[ROW][C]median[/C][C]-0.819166808902777[/C][/ROW]
[ROW][C]mean[/C][C]-4.60135402002848e-17[/C][/ROW]
[ROW][C]Q3[/C][C]5.72062817524456[/C][/ROW]
[ROW][C]maximum[/C][C]17.5134865514515[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51999&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51999&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-12.8975655528088
Q1-6.87424135828476
median-0.819166808902777
mean-4.60135402002848e-17
Q35.72062817524456
maximum17.5134865514515


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')