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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, 03 Nov 2009 11:13:33 -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/03/t12572722147ngxlj52e3h6qt4.htm/, Retrieved Wed, 01 May 2024 22:35:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53289, Retrieved Wed, 01 May 2024 22:35:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact170

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] [SHw WS5 ] [2009-11-03 18:13:33] [d9efc2d105d810fc0b0ac636e31105d1] [Current]
- RMPD    [Trivariate Scatterplots] [SHw WS5] [2009-11-03 18:36:47] [af2352cd9a951bedd08ebe247d0de1a2]
-   PD      [Trivariate Scatterplots] [WS5] [2009-11-27 13:42:05] [af2352cd9a951bedd08ebe247d0de1a2]
-    D    [Bivariate Explorative Data Analysis] [WS5] [2009-11-27 14:06:18] [af2352cd9a951bedd08ebe247d0de1a2]
-    D      [Bivariate Explorative Data Analysis] [WS5] [2009-11-27 14:12:45] [af2352cd9a951bedd08ebe247d0de1a2]
-    D        [Bivariate Explorative Data Analysis] [WS5] [2009-11-27 14:26:23] [af2352cd9a951bedd08ebe247d0de1a2]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
182
213
227
209
219
221
114
97
205
215
224
189
182
201
198
173
238
258
122
101
259
243
188
173
224
215
196
159
187
208
131
93
210
228
176
195
188
188
190
188
176
225
93
79
235
247
195
197
211
166
209
180
185
303
129
85
249
231
212
240
Dataseries Y:
Download CSV
Histogram 
89
97
154
81
110
116
73
73
174
103
130
91
136
106
136
122
131
135
75
68
143
115
93
128
152
125
107
116
220
137
34
51
153
145
116
145
98
118
139
140
113
149
79
47
166
180
122
134
114
125
181
142
143
187
137
62
239
157
139
187

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'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 & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53289&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53289&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53289&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Model: Y[t] = c + b X[t] + e[t]
c12.2885242914561
b0.587475034208875

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 12.2885242914561 \tabularnewline
b & 0.587475034208875 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53289&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]12.2885242914561[/C][/ROW]
[ROW][C]b[/C][C]0.587475034208875[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53289&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53289&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]
c12.2885242914561
b0.587475034208875






Descriptive Statistics about e[t]
# observations60
minimum-55.2477537728187
Q1-21.191826362765
median0.0459837442856966
mean4.03554504628071e-16
Q315.1160694141109
maximum97.8536443114842

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -55.2477537728187 \tabularnewline
Q1 & -21.191826362765 \tabularnewline
median & 0.0459837442856966 \tabularnewline
mean & 4.03554504628071e-16 \tabularnewline
Q3 & 15.1160694141109 \tabularnewline
maximum & 97.8536443114842 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53289&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]-55.2477537728187[/C][/ROW]
[ROW][C]Q1[/C][C]-21.191826362765[/C][/ROW]
[ROW][C]median[/C][C]0.0459837442856966[/C][/ROW]
[ROW][C]mean[/C][C]4.03554504628071e-16[/C][/ROW]
[ROW][C]Q3[/C][C]15.1160694141109[/C][/ROW]
[ROW][C]maximum[/C][C]97.8536443114842[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53289&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53289&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-55.2477537728187
Q1-21.191826362765
median0.0459837442856966
mean4.03554504628071e-16
Q315.1160694141109
maximum97.8536443114842


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 1 ;
Parameters (R input):
par1 = 0 ; par2 = 1 ;
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')