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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 14:30:48 -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/t1257284030wrgswt7rmedcshg.htm/, Retrieved Wed, 01 May 2024 22:17:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53433, Retrieved Wed, 01 May 2024 22:17:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact132

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] [2009-11-03 21:30:48] [6c94b261890ba36343a04d1029691995] [Current]
- R  D    [Bivariate Explorative Data Analysis] [ws5] [2009-11-07 16:00:03] [3fc64fd7a52ce121dfe13dba27bf6e5b]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
123.560
122.117
121.782
121.789
122.273
121.683
119.869
118.873
117.607
122.783
124.454
127.064
125.166
124.554
124.272
128.836
127.408
126.420
124.465
124.526
124.379
130.189
132.196
134.893
132.709
129.955
127.947
130.369
129.852
124.278
126.141
121.743
110.898
117.707
120.738
121.445
120.439
116.313
117.173
119.773
119.639
113.006
113.776
107.866
106.924
114.562
115.367
116.602
114.393
115.140
117.623
119.361
120.527
121.660
122.852
119.325
119.151
126.494
127.832
128.780
Dataseries Y:
Download CSV
Histogram 
111.632
106.707
108.827
108.413
106.249
104.861
102.382
100.320
100.228
117.089
121.523
114.948
112.831
107.605
108.928
101.993
102.850
99.925
101.536
99.450
98.305
110.159
109.483
106.810
96.279
91.982
90.276
90.999
86.622
83.117
80.367
77.550
77.443
92.844
92.175
84.822
81.632
78.872
81.485
80.651
78.192
76.844
76.335
71.415
73.889
86.822
86.371
83.469
82.662
82.880
89.406
95.378
97.657
100.247
99.180
97.493
101.628
114.585
115.669
111.311

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53433&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]3 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=53433&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53433&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Model: Y[t] = c + b X[t] + e[t]
c-64.3457752923572
b1.31324897589451

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -64.3457752923572 \tabularnewline
b & 1.31324897589451 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53433&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-64.3457752923572[/C][/ROW]
[ROW][C]b[/C][C]1.31324897589451[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53433&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53433&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-64.3457752923572
b1.31324897589451






Descriptive Statistics about e[t]
# observations60
minimum-20.9417637759522
Q1-7.42306559821591
median0.0499317232980296
mean-4.1413631783153e-16
Q39.33412539029003
maximum22.4296872463819

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -20.9417637759522 \tabularnewline
Q1 & -7.42306559821591 \tabularnewline
median & 0.0499317232980296 \tabularnewline
mean & -4.1413631783153e-16 \tabularnewline
Q3 & 9.33412539029003 \tabularnewline
maximum & 22.4296872463819 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53433&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]-20.9417637759522[/C][/ROW]
[ROW][C]Q1[/C][C]-7.42306559821591[/C][/ROW]
[ROW][C]median[/C][C]0.0499317232980296[/C][/ROW]
[ROW][C]mean[/C][C]-4.1413631783153e-16[/C][/ROW]
[ROW][C]Q3[/C][C]9.33412539029003[/C][/ROW]
[ROW][C]maximum[/C][C]22.4296872463819[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53433&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53433&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-20.9417637759522
Q1-7.42306559821591
median0.0499317232980296
mean-4.1413631783153e-16
Q39.33412539029003
maximum22.4296872463819


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')