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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 16:45:19 -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/t1256683571l9qnrc45567szbv.htm/, Retrieved Tue, 07 May 2024 09:44:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51301, Retrieved Tue, 07 May 2024 09:44:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact195

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]
-   PD  [Bivariate Data Series] [Reproduce: part 1] [2009-10-27 19:04:39] [f924a0adda9c1905a1ba8f1c751261ff]
- RMP     [Bivariate Explorative Data Analysis] [Bivariate EDA: Pa...] [2009-10-27 21:03:03] [f924a0adda9c1905a1ba8f1c751261ff]
-    D        [Bivariate Explorative Data Analysis] [Bivariate EDA: Pa...] [2009-10-27 22:45:19] [ac86848d66148c9c4c9404e0c9a511eb] [Current]
-    D          [Bivariate Explorative Data Analysis] [Bivariate data: P...] [2009-10-27 23:20:35] [f924a0adda9c1905a1ba8f1c751261ff]
- RMPD            [Harrell-Davis Quantiles] [Harell-Davis quan...] [2009-10-28 00:09:16] [f924a0adda9c1905a1ba8f1c751261ff]
- RMPD            [Harrell-Davis Quantiles] [Harell-Davis quan...] [2009-10-28 00:11:39] [f924a0adda9c1905a1ba8f1c751261ff]
- RMPD            [Harrell-Davis Quantiles] [Harell-Davis quan...] [2009-10-28 00:13:23] [f924a0adda9c1905a1ba8f1c751261ff]
- RMPD              [Univariate Explorative Data Analysis] [Unvariate EDA: pa...] [2009-10-28 00:50:15] [f924a0adda9c1905a1ba8f1c751261ff]
- RMPD          [Pearson Correlation] [Pearson Correlati...] [2009-10-27 23:25:04] [f924a0adda9c1905a1ba8f1c751261ff]
- RMPD          [Kendall tau Rank Correlation] [Kendall Rang corr...] [2009-10-27 23:26:17] [f924a0adda9c1905a1ba8f1c751261ff]
-    D          [Bivariate Explorative Data Analysis] [Bivariate data: P...] [2009-10-27 23:29:27] [f924a0adda9c1905a1ba8f1c751261ff]
- RMPD          [Pearson Correlation] [Pearson Correlati...] [2009-10-27 23:31:21] [f924a0adda9c1905a1ba8f1c751261ff]
-   PD            [Pearson Correlation] [Ws4-2PearX] [2009-10-28 19:33:32] [a94022e7c2399c0f4d62eea578db3411]
-   PD            [Pearson Correlation] [WS4-2-PearLn] [2009-10-28 19:39:45] [a94022e7c2399c0f4d62eea578db3411]
-   PD            [Pearson Correlation] [WS4-2-Pear^2] [2009-10-28 19:43:02] [a94022e7c2399c0f4d62eea578db3411]
- RMPD          [Kendall tau Rank Correlation] [Kendall Rang corr...] [2009-10-27 23:32:47] [f924a0adda9c1905a1ba8f1c751261ff]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
109.87
95.74
123.06
123.39
120.28
115.33
110.4
114.49
132.03
123.16
118.82
128.32
112.24
104.53
132.57
122.52
131.8
124.55
120.96
122.6
145.52
118.57
134.25
136.7
121.37
111.63
134.42
137.65
137.86
119.77
130.69
128.28
147.45
128.42
136.9
143.95
135.64
122.48
136.83
153.04
142.71
123.46
144.37
146.15
147.61
158.51
147.4
165.05
154.64
126.2
157.36
154.15
123.21
113.07
110.45
113.57
122.44
114.93
111.85
126.04
Dataseries Y:
Download CSV
Histogram 
79.8
83.4
113.6
112.9
104.00
109.9
99.00
106.3
128.9
111.1
102.9
130.00
87.00
87.5
117.6
103.4
110.8
112.6
102.5
112.4
135.6
105.1
127.7
137.00
91.00
90.5
122.4
123.3
124.3
120.00
118.1
119.00
142.7
123.6
129.6
151.6
110.4
99.2
130.5
136.2
129.7
128.00
121.6
135.8
143.8
147.5
136.2
156.6
123.3
104.5
139.8
136.5
112.1
118.5
94.4
102.3
111.4
99.2
87.8
115.8

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51301&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51301&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51301&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'George Udny Yule' @ 72.249.76.132






Model: Y[t] = c + b X[t] + e[t]
c-19.5460387116700
b1.05414769523628

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -19.5460387116700 \tabularnewline
b & 1.05414769523628 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51301&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-19.5460387116700[/C][/ROW]
[ROW][C]b[/C][C]1.05414769523628[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51301&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51301&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-19.5460387116700
b1.05414769523628






Descriptive Statistics about e[t]
# observations60
minimum-20.1673608796681
Q1-5.49293101990515
median0.590938500106283
mean9.84484484674771e-16
Q33.77527231037609
maximum19.4014779824078

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -20.1673608796681 \tabularnewline
Q1 & -5.49293101990515 \tabularnewline
median & 0.590938500106283 \tabularnewline
mean & 9.84484484674771e-16 \tabularnewline
Q3 & 3.77527231037609 \tabularnewline
maximum & 19.4014779824078 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51301&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]-20.1673608796681[/C][/ROW]
[ROW][C]Q1[/C][C]-5.49293101990515[/C][/ROW]
[ROW][C]median[/C][C]0.590938500106283[/C][/ROW]
[ROW][C]mean[/C][C]9.84484484674771e-16[/C][/ROW]
[ROW][C]Q3[/C][C]3.77527231037609[/C][/ROW]
[ROW][C]maximum[/C][C]19.4014779824078[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51301&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51301&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-20.1673608796681
Q1-5.49293101990515
median0.590938500106283
mean9.84484484674771e-16
Q33.77527231037609
maximum19.4014779824078


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')