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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 computationMon, 09 Nov 2009 06:21:04 -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/09/t12577729800wjz8yy8q1p7d1o.htm/, Retrieved Thu, 28 Mar 2024 20:35:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=54811, Retrieved Thu, 28 Mar 2024 20:35:10 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact201
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Box-Cox Linearity Plot] [3/11/2009] [2009-11-02 21:47:57] [b98453cac15ba1066b407e146608df68]
- RMPD    [Bivariate Explorative Data Analysis] [] [2009-11-09 13:21:04] [9f6463b67b1eb7bae5c03a796abf0348] [Current]
-   PD      [Bivariate Explorative Data Analysis] [W6 Bivariate EDA] [2009-11-13 15:34:19] [d31db4f83c6a129f6d3e47077769e868]
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Dataseries X:
100
97.82226485
94.04971502
91.12460521
93.13202153
93.88342812
92.55349954
94.43494835
96.25017563
100.4355715
101.5036685
99.39789728
99.68990733
101.6895041
103.6652759
103.0532766
100.9500712
102.345366
101.6472299
99.56809393
95.67727392
96.58494865
96.32604937
95.37109101
96.00056203
96.88367859
94.85280372
92.46943974
93.99180173
93.45262168
92.26698759
90.39653498
90.43001228
91.04995327
89.07845784
89.69314509
87.92459054
85.8789319
83.20612366
83.85722053
83.01393462
82.84508195
78.68864276
77.56959675
78.53689529
78.55717715
77.4761291
81.58931659
85.02428326
91.71290159
95.96293061
90.84689022
92.28788036
95.56511274
93.62452884
92.63071726
89.50914211
87.17171779
86.72624975
85.63212844
Dataseries Y:
100
96.21064363
96.31280765
107.1793443
114.9066592
92.56060184
114.9995356
107.1236185
117.7765394
107.3650971
106.2970187
114.5072908
98.0031578
103.0649206
100.2879168
104.6066685
111.1544534
104.9874617
109.9284852
111.5352466
132.4974459
100.3436426
123.0983561
114.2379493
104.569518
109.0833101
106.9843039
133.6769759
124.8537197
122.5132349
116.8013374
116.0118882
129.7575926
125.1973623
143.7912139
127.9465032
130.2962757
108.4424631
129.3675118
143.6797622
131.8844618
117.6186496
118.9560695
104.8202842
134.624315
140.401226
143.8005015
153.4317823
153.2924677
127.3149438
153.5525216
136.9276493
131.7730101
144.3391845
107.4208229
113.6249652
124.2221603
102.0618557
96.36853348
111.6838488




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=54811&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=54811&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54811&T=0

As an alternative you can also use a 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]
c221.603731633023
b-1.11914602538942

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 221.603731633023 \tabularnewline
b & -1.11914602538942 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54811&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]221.603731633023[/C][/ROW]
[ROW][C]b[/C][C]-1.11914602538942[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54811&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54811&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Model: Y[t] = c + b X[t] + e[t]
c221.603731633023
b-1.11914602538942







Descriptive Statistics about e[t]
# observations60
minimum-29.9717415392007
Q1-9.4514336025847
median-1.08688334457450
mean8.22909448916498e-16
Q37.40758544244647
maximum39.3453223438789

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -29.9717415392007 \tabularnewline
Q1 & -9.4514336025847 \tabularnewline
median & -1.08688334457450 \tabularnewline
mean & 8.22909448916498e-16 \tabularnewline
Q3 & 7.40758544244647 \tabularnewline
maximum & 39.3453223438789 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54811&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]-29.9717415392007[/C][/ROW]
[ROW][C]Q1[/C][C]-9.4514336025847[/C][/ROW]
[ROW][C]median[/C][C]-1.08688334457450[/C][/ROW]
[ROW][C]mean[/C][C]8.22909448916498e-16[/C][/ROW]
[ROW][C]Q3[/C][C]7.40758544244647[/C][/ROW]
[ROW][C]maximum[/C][C]39.3453223438789[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54811&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54811&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Descriptive Statistics about e[t]
# observations60
minimum-29.9717415392007
Q1-9.4514336025847
median-1.08688334457450
mean8.22909448916498e-16
Q37.40758544244647
maximum39.3453223438789



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')