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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 computationMon, 02 Nov 2009 07:52:20 -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/02/t1257173611u7buh8z48xpakkn.htm/, Retrieved Fri, 03 May 2024 15:33:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=52689, Retrieved Fri, 03 May 2024 15:33:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact129

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
-   PD  [Bivariate Data Series] [Reproduction Part 1] [2009-10-26 18:51:43] [96e597a9107bfe8c07649cce3d4f6fec]
- RMPD    [Bivariate Explorative Data Analysis] [JJ Workshop 4, De...] [2009-10-26 19:42:48] [96e597a9107bfe8c07649cce3d4f6fec]
-    D      [Bivariate Explorative Data Analysis] [JJ Workshop 4, de...] [2009-10-27 18:56:34] [96e597a9107bfe8c07649cce3d4f6fec]
- RM D        [Bivariate Explorative Data Analysis] [JJ Workshop 5, mo...] [2009-11-02 13:02:09] [96e597a9107bfe8c07649cce3d4f6fec]
-    D            [Bivariate Explorative Data Analysis] [JJ Workshop 5, mo...] [2009-11-02 14:52:20] [e31f2fa83f4a5291b9a51009566cf69b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
160
171.4
192
231.2
250.8
268.4
266.9
268.5
268.2
265.3
253.8
243.4
213.6
221
227.3
221.6
222.1
232.2
229.6
238.9
238.2
223.9
215
211.1
210.6
206.6
207
201.7
204.5
204.5
195.1
205.5
187.5
173.5
172.3
167.5
157.5
151.1
148.5
147.9
145.6
139.8
138.9
141.4
148.7
150.9
147.3
144.5
134
135.1
131.4
128.4
127.6
127.4
124
123.5
128
129.9
127.6
121.8
Dataseries Y:
Download CSV
Histogram 
8892,49
9880,36
13386,49
13642,24
9960,04
9216
13432,81
11902,81
13759,29
12056,04
12723,84
12254,49
10000
12836,89
14981,76
12656,25
10857,64
8556,25
13735,84
11946,49
11257,21
14113,44
11088,09
11236
10404
12746,41
13572,25
13179,04
10100,25
7293,16
13133,16
12078,01
10140,49
13340,25
10140,49
9801
10465,29
11837,44
11214,81
12814,24
9158,49
6544,81
12973,21
9623,61
10567,84
10962,09
9196,81
8949,16
10322,56
10795,21
12166,09
13018,81
9370,24
7638,76
12409,96
9486,76
10588,41
12701,29
9409
9044,01

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=52689&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=52689&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52689&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]
c8430.93393410707
b14.8327560268161

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 8430.93393410707 \tabularnewline
b & 14.8327560268161 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52689&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]8430.93393410707[/C][/ROW]
[ROW][C]b[/C][C]14.8327560268161[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52689&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52689&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]
c8430.93393410707
b14.8327560268161






Descriptive Statistics about e[t]
# observations60
minimum-4171.07254159096
Q1-1098.2863799772
median-82.3439984975234
mean7.47550169914272e-16
Q31451.74296844668
maximum3179.34062099764

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -4171.07254159096 \tabularnewline
Q1 & -1098.2863799772 \tabularnewline
median & -82.3439984975234 \tabularnewline
mean & 7.47550169914272e-16 \tabularnewline
Q3 & 1451.74296844668 \tabularnewline
maximum & 3179.34062099764 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52689&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]-4171.07254159096[/C][/ROW]
[ROW][C]Q1[/C][C]-1098.2863799772[/C][/ROW]
[ROW][C]median[/C][C]-82.3439984975234[/C][/ROW]
[ROW][C]mean[/C][C]7.47550169914272e-16[/C][/ROW]
[ROW][C]Q3[/C][C]1451.74296844668[/C][/ROW]
[ROW][C]maximum[/C][C]3179.34062099764[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52689&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52689&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-4171.07254159096
Q1-1098.2863799772
median-82.3439984975234
mean7.47550169914272e-16
Q31451.74296844668
maximum3179.34062099764


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')