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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 03:51:00 -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/t1257332052er6uyf9kccvou4v.htm/, Retrieved Mon, 29 Apr 2024 13:38:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53533, Retrieved Mon, 29 Apr 2024 13:38:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWorkshop 5
Estimated Impact167

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]
- RMPD  [Bivariate Explorative Data Analysis] [] [2009-10-28 15:16:32] [5482608004c1d7bbf873930172393a2d]
-    D    [Bivariate Explorative Data Analysis] [] [2009-10-28 16:44:36] [5482608004c1d7bbf873930172393a2d]
- RMPD      [Trivariate Scatterplots] [] [2009-11-03 17:57:36] [5482608004c1d7bbf873930172393a2d]
- RMPD        [Bivariate Explorative Data Analysis] [] [2009-11-03 18:16:45] [5482608004c1d7bbf873930172393a2d]
-                 [Bivariate Explorative Data Analysis] [Workshop 5, Bivar...] [2009-11-04 10:51:00] [a53416c107f5e7e1e12bb9940270d09d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2534
2605
2682
2755
2760
2735
2659
2654
2670
2785
2845
2723
2746
2767
2940
2977
2993
2892
2824
2771
2686
2738
2723
2731
2632
2606
2605
2646
2627
2535
2456
2404
2319
2519
2504
2382
2394
2381
2501
2532
2515
2429
2389
2261
2272
2439
2373
2327
2364
2388
2553
2663
2694
2679
2611
2580
2627
2732
2707
2633
Dataseries Y:
Download CSV
Histogram 
10898
11078
11157
11203
11425
11368
11118
11041
10837
11018
11035
11220
11486
11553
11626
11861
11951
11935
11717
11841
11701
11842
11818
12088
11876
11839
11582
11848
11741
11455
11154
10922
10556
10923
11001
10702
10805
10831
10925
11220
11188
10752
10771
10274
10261
10600
10596
10556
10716
10958
11273
11806
11922
12127
11985
11805
11901
12217
12344
12314

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53533&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]2 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=53533&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53533&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Model: Y[t] = c + b X[t] + e[t]
c5651.58583260877
b2.18226574410748

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 5651.58583260877 \tabularnewline
b & 2.18226574410748 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53533&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]5651.58583260877[/C][/ROW]
[ROW][C]b[/C][C]2.18226574410748[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53533&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53533&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]
c5651.58583260877
b2.18226574410748






Descriptive Statistics about e[t]
# observations60
minimum-825.131874594549
Q1-253.659006059233
median-94.2403734801961
mean2.06316445409508e-15
Q3252.051554513127
maximum916.508463156236

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -825.131874594549 \tabularnewline
Q1 & -253.659006059233 \tabularnewline
median & -94.2403734801961 \tabularnewline
mean & 2.06316445409508e-15 \tabularnewline
Q3 & 252.051554513127 \tabularnewline
maximum & 916.508463156236 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53533&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]-825.131874594549[/C][/ROW]
[ROW][C]Q1[/C][C]-253.659006059233[/C][/ROW]
[ROW][C]median[/C][C]-94.2403734801961[/C][/ROW]
[ROW][C]mean[/C][C]2.06316445409508e-15[/C][/ROW]
[ROW][C]Q3[/C][C]252.051554513127[/C][/ROW]
[ROW][C]maximum[/C][C]916.508463156236[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53533&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53533&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-825.131874594549
Q1-253.659006059233
median-94.2403734801961
mean2.06316445409508e-15
Q3252.051554513127
maximum916.508463156236


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 36 ;
Parameters (R input):
par1 = 0 ; par2 = 36 ; par3 = ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')