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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 12:41:52 -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/t1257363780saosfplbkvugbmk.htm/, Retrieved Mon, 29 Apr 2024 11:36:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53830, Retrieved Mon, 29 Apr 2024 11:36:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWS5 KVN
Estimated Impact131

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] [WS5 Bivariate EDA...] [2009-11-04 19:41:52] [f1100e00818182135823a11ccbd0f3b9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
6545,183
5768,992
6727,176
6115,951
6500,807
6040,055
6699,799
6318,335
6024,93
6344,79
5449,747
6001,299
6302,456
5298,587
5989,552
6280,586
5995,489
6104,724
6530,002
5716,223
5950,112
5817,888
5263,521
6020,801
6099,384
5442,395
6136,729
5758,553
5673,479
6347,118
6490,806
6154,812
5898,965
5701,613
5469,038
6110,404
5899,754
5258,878
6436,937
6037,73
6138,977
6255,21
5969,169
6347,172
5992,052
6151,677
5430,779
5971,777
6276,719
5319,69
6692,586
6001,459
6742,163
6485,913
6351,912
6518,189
6578,3
6903,007
6124,807
6006,192
Dataseries Y:
Download CSV
Histogram 
1116,769
2112,077
2236,818
2740,410
4459,006
5475,567
4115,516
5994,941
4208,768
2668,108
1597,828
1759,419
990,975
1873,359
2166,874
2739,628
4554,667
4702,019
3888,894
5456,714
4029,007
2466,443
1348,800
1613,658
925,852
1809,354
1603,775
3072,536
4200,058
4433,796
4879,083
4466,058
3972,851
2210,053
1097,535
1383,348
708,108
1249,717
1366,061
2855,931
3960,223
4160,000
4727,869
4445,694
4192,349
2051,392
1097,705
1389,304
616,954
1125,769
1694,107
2737,042
3904,251
4850,059
4534,207
4666,646
5124,716
2164,238
1238,459
1916,911

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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53830&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53830&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53830&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'Gwilym Jenkins' @ 72.249.127.135






Model: Y[t] = c + b X[t] + e[t]
c-4664.98501907088
b1.24676884762537

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -4664.98501907088 \tabularnewline
b & 1.24676884762537 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53830&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-4664.98501907088[/C][/ROW]
[ROW][C]b[/C][C]1.24676884762537[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53830&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53830&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]
c-4664.98501907088
b1.24676884762537






Descriptive Statistics about e[t]
# observations60
minimum-2543.6786954274
Q1-1056.53679420997
median-170.957829817485
mean1.12232445559357e-13
Q31276.63980684501
maximum2994.89025659123

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -2543.6786954274 \tabularnewline
Q1 & -1056.53679420997 \tabularnewline
median & -170.957829817485 \tabularnewline
mean & 1.12232445559357e-13 \tabularnewline
Q3 & 1276.63980684501 \tabularnewline
maximum & 2994.89025659123 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53830&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]-2543.6786954274[/C][/ROW]
[ROW][C]Q1[/C][C]-1056.53679420997[/C][/ROW]
[ROW][C]median[/C][C]-170.957829817485[/C][/ROW]
[ROW][C]mean[/C][C]1.12232445559357e-13[/C][/ROW]
[ROW][C]Q3[/C][C]1276.63980684501[/C][/ROW]
[ROW][C]maximum[/C][C]2994.89025659123[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53830&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53830&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-2543.6786954274
Q1-1056.53679420997
median-170.957829817485
mean1.12232445559357e-13
Q31276.63980684501
maximum2994.89025659123


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')