Free Statistics

of Irreproducible Research!

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 11:44:27 -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/t1256838336ld608leeficlgm7.htm/, Retrieved Sun, 28 Apr 2024 21:10:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=52025, Retrieved Sun, 28 Apr 2024 21:10:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154
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] [f0f26816ac6124f58333f11f6c174000] [Current]
-  M D      [Bivariate Explorative Data Analysis] [ws 5] [2009-11-01 17:19:37] [b5908418e3090fddbd22f5f0f774653d]
-  M        [Bivariate Explorative Data Analysis] [] [2009-11-04 11:57:05] [08fc5c07292c885b941f0cb515ce13f3]
-  M D      [Bivariate Explorative Data Analysis] [] [2009-11-04 17:11:54] [44aba10e04b8829f2a97df951213e00a]
-  M D      [Bivariate Explorative Data Analysis] [WS5(1)] [2009-11-04 17:11:54] [7d268329e554b8694908ba13e6e6f258]
-    D        [Bivariate Explorative Data Analysis] [WS5(2)] [2009-11-04 17:41:47] [7d268329e554b8694908ba13e6e6f258]
- RM D      [Bivariate Explorative Data Analysis] [Y=f(Z)] [2009-12-28 14:37:02] [2663058f2a5dda519058ac6b2228468f]
-    D        [Bivariate Explorative Data Analysis] [X=f(Z)] [2009-12-28 14:49:17] [2663058f2a5dda519058ac6b2228468f]
- RM D      [Bivariate Explorative Data Analysis] [X=f(Z)] [2009-12-28 14:41:24] [2663058f2a5dda519058ac6b2228468f]
- RM D      [Bivariate Explorative Data Analysis] [model_3a] [2009-12-29 09:34:10] [2663058f2a5dda519058ac6b2228468f]
- RM D      [Bivariate Explorative Data Analysis] [model_3b] [2009-12-29 09:39:08] [2663058f2a5dda519058ac6b2228468f]
- RM D      [Bivariate Explorative Data Analysis] [model_3] [2009-12-29 09:45:53] [2663058f2a5dda519058ac6b2228468f]
Feedback Forum

Post a new message
Dataseries X:
4.1
4.1
4
3.9
3.8
3.8
4
4.4
4.6
4.6
4.6
4.7
4.8
4.8
4.7
4.7
4.7
4.6
5
5.4
5.5
5.6
5.6
5.8
6
6.1
6.1
6
6
6.1
6.5
7.1
7.4
7.4
7.5
7.6
7.8
7.8
7.7
7.6
7.5
7.3
7.6
8
8
7.9
7.8
7.7
7.8
7.7
7.5
7.3
7.1
7
7.3
7.8
7.9
7.9
7.8
7.8
7.9
7.8
7.6
7.4
7.2
6.9
7.1
7.5
7.6
7.4
7.3
7.2
7.3
7.2
7.1
7
6.9
6.8
7.2
7.6
7.7
7.6
7.5
7.5
7.6
7.6
7.6
7.5
7.3
7.2
7.4
8
8.2
8
7.7
7.7
7.8
7.8
7.7
7.5
7.3
7.1
7.1
7.2
6.8
6.6
6.4
6.4
6.5
6.3
5.9
5.5
5.2
4.9
5.4
5.8
5.7
5.6
5.5
5.4
5.4
5.4
5.5
5.8
5.7
5.4
5.6
5.8
6.2
6.8
6.7
6.7
6.4
6.3
6.3
6.4
6.3
6
6.3
6.3
6.6
7.5
7.8
7.9
7.8
7.6
7.5
7.6
7.5
7.3
7.6
7.5
7.6
7.9
7.9
8.1
8.2
8
7.5
6.8
6.5
6.6
7.6
8
8.1
7.7
7.5
7.6
7.8
7.8
7.8
7.5
7.5
7.1
7.5
7.5
7.6
7.7
7.7
7.9
8.1
8.2
8.2
8.2
7.9
7.3
6.9
6.6
6.7
6.9
7
7.1
7.2
7.1
6.9
7
6.8
6.4
6.7
6.6
6.4
6.3
6.2
6.5
6.8
6.8
6.4
6.1
5.8
6.1
7.2
7.3
6.9
6.1
5.8
6.2
7.1
7.7
7.9
7.7
7.4
7.5
8
8.1
Dataseries Y:
3.88
3.98
3.29
2.88
3.22
3.62
3.82
3.54
2.53
2.22
2.85
2.78
2.28
2.26
2.71
2.77
2.77
2.64
2.56
2.07
2.32
2.16
2.23
2.4
2.84
2.77
2.93
2.91
2.69
2.38
2.58
3.19
2.82
2.72
2.53
2.7
2.42
2.5
2.31
2.41
2.56
2.76
2.71
2.44
2.46
2.12
1.99
1.86
1.88
1.82
1.74
1.71
1.38
1.27
1.19
1.28
1.19
1.22
1.47
1.46
1.96
1.88
2.03
2.04
1.9
1.8
1.92
1.92
1.97
2.46
2.36
2.53
2.31
1.98
1.46
1.26
1.58
1.74
1.89
1.85
1.62
1.3
1.42
1.15
0.42
0.74
1.02
1.51
1.86
1.59
1.03
0.44
0.82
0.86
0.58
0.59
0.95
0.98
1.23
1.17
0.84
0.74
0.65
0.91
1.19
1.3
1.53
1.94
1.79
1.95
2.26
2.04
2.16
2.75
2.79
2.88
3.36
2.97
3.1
2.49
2.2
2.25
2.09
2.79
3.14
2.93
2.65
2.67
2.26
2.35
2.13
2.18
2.9
2.63
2.67
1.81
1.33
0.88
1.28
1.26
1.26
1.29
1.1
1.37
1.21
1.74
1.76
1.48
1.04
1.62
1.49
1.79
1.8
1.58
1.86
1.74
1.59
1.26
1.13
1.92
2.61
2.26
2.41
2.26
2.03
2.86
2.55
2.27
2.26
2.57
3.07
2.76
2.51
2.87
3.14
3.11
3.16
2.47
2.57
2.89
2.63
2.38
1.69
1.96
2.19
1.87
1.6
1.63
1.22
1.21
1.49
1.64
1.66
1.77
1.82
1.78
1.28
1.29
1.37
1.12
1.51
2.24
2.94
3.09
3.46
3.64
4.39
4.15
5.21
5.8
5.91
5.39
5.46
4.72
3.14
2.63
2.32
1.93
0.62
0.6
-0.37
-1.1
-1.68
-0.78




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=52025&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=52025&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52025&T=0

As an alternative you can also use a 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]
c4.9502763299208
b-0.416028181457045

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 4.9502763299208 \tabularnewline
b & -0.416028181457045 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52025&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]4.9502763299208[/C][/ROW]
[ROW][C]b[/C][C]-0.416028181457045[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52025&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52025&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Model: Y[t] = c + b X[t] + e[t]
c4.9502763299208
b-0.416028181457045







Descriptive Statistics about e[t]
# observations224
minimum-3.30205087826444
Q1-0.521763559920109
median-0.0732706052843702
mean8.00954354923666e-18
Q30.462644189980579
maximum3.95512657656993

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 224 \tabularnewline
minimum & -3.30205087826444 \tabularnewline
Q1 & -0.521763559920109 \tabularnewline
median & -0.0732706052843702 \tabularnewline
mean & 8.00954354923666e-18 \tabularnewline
Q3 & 0.462644189980579 \tabularnewline
maximum & 3.95512657656993 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52025&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]224[/C][/ROW]
[ROW][C]minimum[/C][C]-3.30205087826444[/C][/ROW]
[ROW][C]Q1[/C][C]-0.521763559920109[/C][/ROW]
[ROW][C]median[/C][C]-0.0732706052843702[/C][/ROW]
[ROW][C]mean[/C][C]8.00954354923666e-18[/C][/ROW]
[ROW][C]Q3[/C][C]0.462644189980579[/C][/ROW]
[ROW][C]maximum[/C][C]3.95512657656993[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52025&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52025&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Descriptive Statistics about e[t]
# observations224
minimum-3.30205087826444
Q1-0.521763559920109
median-0.0732706052843702
mean8.00954354923666e-18
Q30.462644189980579
maximum3.95512657656993



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')