FreeStatistics.org

Free Statistics

of Irreproducible Research!

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, 11 Nov 2009 05:49:51 -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/11/t1257943856iaqtfiqf9ch0hxp.htm/, Retrieved Thu, 25 Apr 2024 21:12:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=55545, Retrieved Thu, 25 Apr 2024 21:12:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact190

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] [] [2009-11-11 12:49:51] [c4328af89eba9af53ee195d6fed304d9] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-20,286
-19,188
-12,489
12,524
-17,320
-18,183
-26,199
-7,417
-6,292
-6,022
-1,695
-0,368
-28,388
-18,524
-12,536
-11,631
-12,291
-8,682
-14,691
-17,360
-21,873
-14,531
-8,445
-1,836
10,500
17,897
18,312
44,853
82,821
57,647
44,737
44,705
64,243
39,752
46,444
67,940
51,209
57,739
74,377
56,231
48,788
42,781
28,965
41,528
27,437
47,397
54,387
56,079
75,792
127,288
184,410
127,691
116,945
118,555
149,008
150,618
80,590
77,625
70,009
79,853
Dataseries Y:
Download CSV
Histogram 
-3,309
-7,848
-7,623
-0,690
-9,257
-7,994
-8,821
-9,128
-5,279
-2,897
-0,449
4,715
-1,789
-3,053
-0,316
-1,543
-1,068
-4,514
-0,368
-1,128
0,954
7,215
5,900
15,042
22,324
31,118
30,150
35,339
53,021
44,876
44,384
47,714
43,508
38,197
40,919
48,959
46,581
51,247
54,084
50,990
53,913
51,607
49,107
52,365
53,633
69,907
81,149
80,530
95,690
113,137
131,453
107,299
98,210
106,509
119,869
113,953
92,043
107,453
73,727
86,374

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=55545&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=55545&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55545&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]
c11.5233971994310
b0.76967421639484

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 11.5233971994310 \tabularnewline
b & 0.76967421639484 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55545&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]11.5233971994310[/C][/ROW]
[ROW][C]b[/C][C]0.76967421639484[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55545&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55545&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]
c11.5233971994310
b0.76967421639484






Descriptive Statistics about e[t]
# observations60
minimum-22.2475854754679
Q1-7.926001241777
median-1.88153692049607
mean-3.39687768654192e-16
Q35.93122924625629
maximum36.1836417529197

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -22.2475854754679 \tabularnewline
Q1 & -7.926001241777 \tabularnewline
median & -1.88153692049607 \tabularnewline
mean & -3.39687768654192e-16 \tabularnewline
Q3 & 5.93122924625629 \tabularnewline
maximum & 36.1836417529197 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55545&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]-22.2475854754679[/C][/ROW]
[ROW][C]Q1[/C][C]-7.926001241777[/C][/ROW]
[ROW][C]median[/C][C]-1.88153692049607[/C][/ROW]
[ROW][C]mean[/C][C]-3.39687768654192e-16[/C][/ROW]
[ROW][C]Q3[/C][C]5.93122924625629[/C][/ROW]
[ROW][C]maximum[/C][C]36.1836417529197[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55545&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55545&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-22.2475854754679
Q1-7.926001241777
median-1.88153692049607
mean-3.39687768654192e-16
Q35.93122924625629
maximum36.1836417529197


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')