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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, 04 Nov 2009 10:11:54 -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/04/t12573552899dl4mw16fhklyu3.htm/, Retrieved Mon, 29 Apr 2024 13:31:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53737, Retrieved Mon, 29 Apr 2024 13:31:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact150

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Trivariate Scatterplots] [SHW_WS5] [2009-10-29 16:26:54] [8b1aef4e7013bd33fbc2a5833375c5f5]
- RMPD  [Bivariate Explorative Data Analysis] [SHW_WS5] [2009-10-29 17:44:27] [8b1aef4e7013bd33fbc2a5833375c5f5]
-  M D      [Bivariate Explorative Data Analysis] [WS5(1)] [2009-11-04 17:11:54] [5edea6bc5a9a9483633d9320282a2734] [Current]
-    D        [Bivariate Explorative Data Analysis] [WS5(2)] [2009-11-04 17:41:47] [7d268329e554b8694908ba13e6e6f258]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
24	
23	
24	
24	
27	
28	
25	
19	
19	
19	
20	
16	
22	
21	
25	
29	
28	
25	
26	
24	
28	
28	
28	
28	
32	
31	
22	
29	
31	
29	
32	
32	
31	
29	
28	
28	
29	
22	
26	
24	
27	
27	
23	
21	
19	
17	
19	
21	
13	
8	
5	
10	
6	
6	
8	
11	
12	
13	
19	
19
Dataseries Y:
Download CSV
Histogram 
10,9
10
9,2
9,2
9,5
9,6
9,5
9,1
8,9
9
10,1
10,3
10,2
9,6
9,2
9,3
9,4
9,4
9,2
9
9
9
9,8
10
9,8
9,3
9
9
9,1
9,1
9,1
9,2
8,8
8,3
8,4
8,1
7,7
7,9
7,9
8
7,9
7,6
7,1
6,8
6,5
6,9
8,2
8,7
8,3
7,9
7,5
7,8
8,3
8,4
8,2
7,7
7,2
7,3
8,1
8,5

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=53737&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=53737&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53737&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]
c7.50909440523457
b0.0533639549558821

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 7.50909440523457 \tabularnewline
b & 0.0533639549558821 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53737&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]7.50909440523457[/C][/ROW]
[ROW][C]b[/C][C]0.0533639549558821[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53737&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53737&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]
c7.50909440523457
b0.0533639549558821






Descriptive Statistics about e[t]
# observations60
minimum-2.02300954939632
Q1-0.468078448047059
median0.0568067208683825
mean-3.61953554332076e-17
Q30.495262540691911
maximum2.11017067582426

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -2.02300954939632 \tabularnewline
Q1 & -0.468078448047059 \tabularnewline
median & 0.0568067208683825 \tabularnewline
mean & -3.61953554332076e-17 \tabularnewline
Q3 & 0.495262540691911 \tabularnewline
maximum & 2.11017067582426 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53737&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]-2.02300954939632[/C][/ROW]
[ROW][C]Q1[/C][C]-0.468078448047059[/C][/ROW]
[ROW][C]median[/C][C]0.0568067208683825[/C][/ROW]
[ROW][C]mean[/C][C]-3.61953554332076e-17[/C][/ROW]
[ROW][C]Q3[/C][C]0.495262540691911[/C][/ROW]
[ROW][C]maximum[/C][C]2.11017067582426[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53737&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53737&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-2.02300954939632
Q1-0.468078448047059
median0.0568067208683825
mean-3.61953554332076e-17
Q30.495262540691911
maximum2.11017067582426


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')