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 computationWed, 04 Nov 2009 11:09:38 -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/t1257358267ina44jnxjcg9yi5.htm/, Retrieved Mon, 29 Apr 2024 08:36:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53776, Retrieved Mon, 29 Apr 2024 08:36:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128

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] [workshop 5 (4)] [2009-11-04 18:09:38] [9a3898f49d4e2f0208d1968305d88f0a] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2
1.8 
2.7 
2.3 
1.9 
2
2.3 
2.8 
2.4 
2.3 
2.7 
2.7 
2.9 
3
2.2 
2.3 
2.8 
2.8 
2.8 
2.2 
2.6 
2.8 
2.5 
2.4 
2.3 
1.9 
1.7 
2
2.1 
1.7 
1.8 
1.8 
1.8 
1.3 
1.3 
1.3
1.2 
1.4 
2.2 
2.9 
3.1 
3.5 
3.6 
4.4 
4.1 
5.1 
5.8 
5.9 
5.4 
5.5 
4.8
3.2 
2.7 
2.1 
1.9 
0.6 
0.7 
-0.2 
-1
-1.7
Dataseries Y:
Download CSV
Histogram 
3956.2
3142.7
3884.3
3892.2
3613
3730.5
3481.3
3649.5
4215.2
4066.6
4196.8
4536.6
4441.6
3548.3
4735.9
4130.6
4356.2
4159.6
3988
4167.8
4902.2
3909.4
4697.6
4308.9
4420.4
3544.2
4433
4479.7
4533.2
4237.5
4207.4
4394
5148.4
4202.2
4682.5
4884.3
5288.9
4505.2
4611.5
5104
4586.6
4529.3
4504.1
4604.9
4795.4
5391.1
5213.9
5415
5990.3
4241.8
5677.6
5164.2
3962.3
4011
3310.3
3837.3
4145.3
3796.7
3849.6
4285

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53776&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]5 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=53776&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53776&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 time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Model: Y[t] = c + b X[t] + e[t]
c3877.01894274973
b197.27247920635

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 3877.01894274973 \tabularnewline
b & 197.27247920635 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53776&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]3877.01894274973[/C][/ROW]
[ROW][C]b[/C][C]197.27247920635[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53776&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53776&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]
c3877.01894274973
b197.27247920635






Descriptive Statistics about e[t]
# observations60
minimum-1089.40940532116
Q1-289.059337102911
median-16.1092688846559
mean1.51499183568641e-14
Q3307.211167055820
maximum1175.15408220265

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -1089.40940532116 \tabularnewline
Q1 & -289.059337102911 \tabularnewline
median & -16.1092688846559 \tabularnewline
mean & 1.51499183568641e-14 \tabularnewline
Q3 & 307.211167055820 \tabularnewline
maximum & 1175.15408220265 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53776&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]-1089.40940532116[/C][/ROW]
[ROW][C]Q1[/C][C]-289.059337102911[/C][/ROW]
[ROW][C]median[/C][C]-16.1092688846559[/C][/ROW]
[ROW][C]mean[/C][C]1.51499183568641e-14[/C][/ROW]
[ROW][C]Q3[/C][C]307.211167055820[/C][/ROW]
[ROW][C]maximum[/C][C]1175.15408220265[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53776&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53776&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-1089.40940532116
Q1-289.059337102911
median-16.1092688846559
mean1.51499183568641e-14
Q3307.211167055820
maximum1175.15408220265


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')