FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationSat, 24 Oct 2009 05:34:59 -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/24/t12563842038c9sialxu18qz2o.htm/, Retrieved Fri, 03 May 2024 07:26:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=50076, Retrieved Fri, 03 May 2024 07:26:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWorkshop 4 deel 2
Estimated Impact170

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]
- RMPD    [Bivariate Explorative Data Analysis] [Workshop 4] [2009-10-24 11:34:59] [e7a989b306049c061a54f626f1127c12] [Current]
-           [Bivariate Explorative Data Analysis] [] [2009-10-27 17:30:30] [e0a128c302a1ec9189220a385b8da313]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1.1
1.15
1.1
1
1.45
1.25
1.05
1.1
1.25
1.5
1.3
1.25
1.45
1.45
1.55
1.6
1.2
1.25
1.5
1.5
1.5
1.2
1.4
1.5
1.35
1.3
1.25
1.05
0.95
1.1
1.15
0.95
1
1
1
0.75
0.75
0.75
0.7
0.8
1.2
1.55
1.65
1.85
1.9
2.3
2.15
2.65
3
3.05
2.8
2.85
2.5
1.7
1.45
1.15
1.05
0.4
0.45
0
Dataseries Y:
Download CSV
Histogram 
65.35
58.6
55.4
55.7
54.1
54.4
55.1
54.75
54.75
58
55.6
56.05
57
59.55
57.05
57.55
57.7
55.4
58
59.6
63.25
63.9
65.65
70.15
68.65
71.5
67.25
69.95
79.65
85.2
87.5
87.9
90.45
90.15
84.8
86.15
92.4
88.85
92.3
105.7
107.65
107.95
122.35
129.65
144.5
155.45
160.5
157.55
166.6
157.05
142.35
136.95
108
98.2
95.45
103.2
98.15
99.75
99.45
107.2

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=50076&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=50076&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=50076&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]
c45.7495495395045
b30.1320941953036

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 45.7495495395045 \tabularnewline
b & 30.1320941953036 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=50076&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]45.7495495395045[/C][/ROW]
[ROW][C]b[/C][C]30.1320941953036[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=50076&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=50076&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]
c45.7495495395045
b30.1320941953036






Descriptive Statistics about e[t]
# observations60
minimum-36.4109002519903
Q1-24.1606555092211
median3.25042565171822
mean5.74309118780055e-16
Q321.2709492005441
maximum61.4504504604955

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -36.4109002519903 \tabularnewline
Q1 & -24.1606555092211 \tabularnewline
median & 3.25042565171822 \tabularnewline
mean & 5.74309118780055e-16 \tabularnewline
Q3 & 21.2709492005441 \tabularnewline
maximum & 61.4504504604955 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=50076&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]-36.4109002519903[/C][/ROW]
[ROW][C]Q1[/C][C]-24.1606555092211[/C][/ROW]
[ROW][C]median[/C][C]3.25042565171822[/C][/ROW]
[ROW][C]mean[/C][C]5.74309118780055e-16[/C][/ROW]
[ROW][C]Q3[/C][C]21.2709492005441[/C][/ROW]
[ROW][C]maximum[/C][C]61.4504504604955[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=50076&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=50076&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-36.4109002519903
Q1-24.1606555092211
median3.25042565171822
mean5.74309118780055e-16
Q321.2709492005441
maximum61.4504504604955


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')