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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 computationFri, 13 Nov 2009 08:10:26 -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/13/t12581250674amlioeymftgu33.htm/, Retrieved Sun, 05 May 2024 19:53:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=56794, Retrieved Sun, 05 May 2024 19:53:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact111

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 6] [2009-11-13 15:10:26] [6c94b261890ba36343a04d1029691995] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
93.028
92.285
91.685
94.260
93.858
92.437
92.980
92.099
92.803
88.551
98.334
98.329
96.455
97.109
97.687
98.512
98.673
96.028
98.014
95.580
97.838
97.760
99.913
97.588
93.942
93.656
92.881
93.120
91.063
90.930
91.946
94.624
65.484
95.862
95.530
94.574
94.677
93.845
91.533
91.214
90.922
89.563
89.945
91.850
92.505
92.437
93.876
93.561
94.119
95.264
96.089
97.160
98.644
96.266
97.938
99.757
101.550
102.449
102.416
102.587
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=56794&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=56794&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56794&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-22.8196798817829
b1.25463433159876

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -22.8196798817829 \tabularnewline
b & 1.25463433159876 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56794&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-22.8196798817829[/C][/ROW]
[ROW][C]b[/C][C]1.25463433159876[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56794&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56794&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-22.8196798817829
b1.25463433159876






Descriptive Statistics about e[t]
# observations60
minimum-21.0034834755636
Q1-9.07597057164874
median-1.25146556975694
mean3.39073387423117e-16
Q38.65845774187414
maximum28.8095551843807

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -21.0034834755636 \tabularnewline
Q1 & -9.07597057164874 \tabularnewline
median & -1.25146556975694 \tabularnewline
mean & 3.39073387423117e-16 \tabularnewline
Q3 & 8.65845774187414 \tabularnewline
maximum & 28.8095551843807 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56794&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]-21.0034834755636[/C][/ROW]
[ROW][C]Q1[/C][C]-9.07597057164874[/C][/ROW]
[ROW][C]median[/C][C]-1.25146556975694[/C][/ROW]
[ROW][C]mean[/C][C]3.39073387423117e-16[/C][/ROW]
[ROW][C]Q3[/C][C]8.65845774187414[/C][/ROW]
[ROW][C]maximum[/C][C]28.8095551843807[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56794&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56794&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-21.0034834755636
Q1-9.07597057164874
median-1.25146556975694
mean3.39073387423117e-16
Q38.65845774187414
maximum28.8095551843807


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')