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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_edabi.wasp
Title produced by softwareBivariate Explorative Data Analysis
Date of computationFri, 13 Nov 2009 11:43:15 -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/13/t1258137830rlbz12pf75g1qrd.htm/, Retrieved Sun, 05 May 2024 20:12:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=56990, Retrieved Sun, 05 May 2024 20:12:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact107

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] [3/11/2009] [2009-11-02 22:01:32] [b98453cac15ba1066b407e146608df68]
-   PD    [Bivariate Explorative Data Analysis] [WS 6.6] [2009-11-13 18:43:15] [29af64a72952b0c5025d716b5179273f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
126,2
127,0
127,1
123,6
125,0
134,7
140,7
137,9
142,1
137,5
136,4
140,7
120,0
123,8
132,2
136,4
144,2
144,9
138,9
129,3
133,3
137,5
140,0
146,3
124,6
130,2
139,0
149,1
151,9
149,0
127,8
113,8
117,5
123,2
127,3
131,5
110,8
112,7
116,9
127,3
130,8
130,6
124,1
113,8
112,3
112,5
112,7
120,4
104,6
107,9
108,5
110,9
111,5
124,5
133,3
125,9
121,1
108,9
105,5
114,8
Dataseries Y:
Download CSV
Histogram 
111,2
116,7
114,8
100,0
98,8
106,3
119,5
120,7
121,1
112,4
108,2
113,6
106,7
114,3
117,3
109,6
109,9
112,5
116,1
112,0
113,3
107,9
108,2
114,8
105,6
111,9
113,6
108,4
111,1
112,5
112,6
108,7
108,9
104,5
105,9
111,1
102,2
108,3
112,3
110,8
108,6
103,8
96,6
88,0
85,6
88,8
92,9
98,8
88,8
90,5
87,7
81,9
80,2
86,3
94,3
94,6
92,2
88,8
88,2
96,3

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'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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56990&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56990&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56990&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'Gwilym Jenkins' @ 72.249.127.135






Model: Y[t] = c + b X[t] + e[t]
c25.0707295629389
b0.625642820242439

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 25.0707295629389 \tabularnewline
b & 0.625642820242439 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56990&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]25.0707295629389[/C][/ROW]
[ROW][C]b[/C][C]0.625642820242439[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56990&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56990&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]
c25.0707295629389
b0.625642820242439






Descriptive Statistics about e[t]
# observations60
minimum-16.6632606831226
Q1-4.67030545974374
median-1.20326768206810
mean-4.26227882560174e-16
Q36.16403597238642
maximum14.0916247507199

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -16.6632606831226 \tabularnewline
Q1 & -4.67030545974374 \tabularnewline
median & -1.20326768206810 \tabularnewline
mean & -4.26227882560174e-16 \tabularnewline
Q3 & 6.16403597238642 \tabularnewline
maximum & 14.0916247507199 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56990&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]-16.6632606831226[/C][/ROW]
[ROW][C]Q1[/C][C]-4.67030545974374[/C][/ROW]
[ROW][C]median[/C][C]-1.20326768206810[/C][/ROW]
[ROW][C]mean[/C][C]-4.26227882560174e-16[/C][/ROW]
[ROW][C]Q3[/C][C]6.16403597238642[/C][/ROW]
[ROW][C]maximum[/C][C]14.0916247507199[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56990&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56990&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-16.6632606831226
Q1-4.67030545974374
median-1.20326768206810
mean-4.26227882560174e-16
Q36.16403597238642
maximum14.0916247507199


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')