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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 computationTue, 27 Oct 2009 09:56:48 -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/27/t1256659176qba9obdjb1jg41g.htm/, Retrieved Tue, 07 May 2024 12:12:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51025, Retrieved Tue, 07 May 2024 12:12:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact102

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 4/part 2] [2009-10-27 15:56:48] [bebfa40a4e66abcf3fcee16e050bb8d6] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
27501
26577
25705
25147
24740
24281
23577
22598
21903
21360
24226
25468
26858
25684
24938
24347
25137
24479
23810
23301
23076
23049
25870
27291
28706
28220
26612
25453
26066
25696
23851
24203
22433
19195
21955
23326
24490
23996
22181
22550
23013
22826
20544
20814
18518
17964
21139
21818
23028
21890
21833
22289
22380
22316
22614
22775
21221
20915
23889
24703
Dataseries Y:
Download CSV
Histogram 
10.22197765
10.18780146
10.1544408
10.13249388
10.11617665
10.09744943
10.06802694
10.02561669
9.994378893
9.969275293
10.09518172
10.14517804
10.19831901
10.15362351
10.12414802
10.10016392
10.13209614
10.10557089
10.07786094
10.05625156
10.0465484
10.04537766
10.16083928
10.21431226
10.26486144
10.24778623
10.18911752
10.14458889
10.16838706
10.15409062
10.07958142
10.09423187
10.01828837
9.862405107
9.996750183
10.0573239
10.10602015
10.08564243
10.00699135
10.02349034
10.04381455
10.03565552
9.930324206
9.943381116
9.826498511
9.796125034
9.958874955
9.990490596
10.04446615
9.993785191
9.991177866
10.01184856
10.01592298
10.01305919
10.02632446
10.03341872
9.962746536
9.948221884
10.08117338
10.11467997

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51025&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]2 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=51025&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51025&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Model: Y[t] = c + b X[t] + e[t]
c9.0526087242152
b4.28766521137769e-05

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 9.0526087242152 \tabularnewline
b & 4.28766521137769e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51025&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]9.0526087242152[/C][/ROW]
[ROW][C]b[/C][C]4.28766521137769e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51025&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51025&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]
c9.0526087242152
b4.28766521137769e-05






Descriptive Statistics about e[t]
# observations60
minimum-0.0267198687870995
Q1-0.000481308699539173
median0.00271370958177596
mean1.28163143527469e-19
Q30.00403017829375541
maximum0.00457438857882977

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -0.0267198687870995 \tabularnewline
Q1 & -0.000481308699539173 \tabularnewline
median & 0.00271370958177596 \tabularnewline
mean & 1.28163143527469e-19 \tabularnewline
Q3 & 0.00403017829375541 \tabularnewline
maximum & 0.00457438857882977 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51025&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]-0.0267198687870995[/C][/ROW]
[ROW][C]Q1[/C][C]-0.000481308699539173[/C][/ROW]
[ROW][C]median[/C][C]0.00271370958177596[/C][/ROW]
[ROW][C]mean[/C][C]1.28163143527469e-19[/C][/ROW]
[ROW][C]Q3[/C][C]0.00403017829375541[/C][/ROW]
[ROW][C]maximum[/C][C]0.00457438857882977[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51025&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51025&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-0.0267198687870995
Q1-0.000481308699539173
median0.00271370958177596
mean1.28163143527469e-19
Q30.00403017829375541
maximum0.00457438857882977


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')