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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 10:04:21 -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/t1257354390e0jknttetym1r1h.htm/, Retrieved Mon, 29 Apr 2024 14:13:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53728, Retrieved Mon, 29 Apr 2024 14:13:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144

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] [EDA] [2009-11-04 17:04:21] [99bf2a1e962091d45abf4c2600a412f9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4651,600942
4648,46719
4629,664677
4595,193403
4573,257137
4592,05965
4751,881013
4805,154801
4811,422306
4805,154801
4751,881013
4755,014766
4742,479757
4736,212252
4720,543491
4686,072217
4667,269704
4673,537208
4836,492323
4861,562341
4858,428589
4808,288553
4755,014766
4761,28227
4748,747261
4739,346005
4708,008482
4689,205969
4686,072217
4686,072217
4833,358571
4852,161084
4833,358571
4733,0785
4664,135951
4635,932181
4648,46719
4610,862164
4557,588376
4538,785863
4491,779579
4454,174553
4629,664677
4661,002199
4588,925898
4541,919615
4488,645827
4501,180836
4510,582093
4482,378323
4435,37204
4425,970783
4360,161986
4388,365756
4545,053367
4563,85588
4513,715845
4476,110818
4463,57581
4507,448341
Dataseries Y:
Download CSV
Histogram 
52.034
58.146
79.035
63.969
34.831
42.602
23.589
21.974
29.651
39.547
27.957
10.511
63.044
50.898
63.547
55.028
40.073
56.080
28.091
29.211
33.415
35.336
31.502
1.007
75.840
69.091
88.380
61.147
59.750
50.533
33.467
28.944
32.053
42.095
27.840
-686
69.362
55.107
73.801
50.981
48.575
52.660
37.173
25.894
30.201
46.248
26.577
1.911
68.401
65.458
67.183
76.063
48.064
52.591
34.502
22.268
30.174
39.902
14.115
-2.715

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=53728&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=53728&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53728&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]
c121.331597484739
b-0.0193459066688307

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 121.331597484739 \tabularnewline
b & -0.0193459066688307 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53728&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]121.331597484739[/C][/ROW]
[ROW][C]b[/C][C]-0.0193459066688307[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53728&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53728&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]
c121.331597484739
b-0.0193459066688307






Descriptive Statistics about e[t]
# observations60
minimum-717.645286188085
Q1-0.116989026062119
median10.5958246396320
mean-5.73615229389664e-17
Q327.3260907617775
maximum58.1290952040959

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -717.645286188085 \tabularnewline
Q1 & -0.116989026062119 \tabularnewline
median & 10.5958246396320 \tabularnewline
mean & -5.73615229389664e-17 \tabularnewline
Q3 & 27.3260907617775 \tabularnewline
maximum & 58.1290952040959 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53728&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]-717.645286188085[/C][/ROW]
[ROW][C]Q1[/C][C]-0.116989026062119[/C][/ROW]
[ROW][C]median[/C][C]10.5958246396320[/C][/ROW]
[ROW][C]mean[/C][C]-5.73615229389664e-17[/C][/ROW]
[ROW][C]Q3[/C][C]27.3260907617775[/C][/ROW]
[ROW][C]maximum[/C][C]58.1290952040959[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53728&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53728&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-717.645286188085
Q1-0.116989026062119
median10.5958246396320
mean-5.73615229389664e-17
Q327.3260907617775
maximum58.1290952040959


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')