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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_bidensity.wasp
Title produced by softwareBivariate Kernel Density Estimation
Date of computationThu, 13 Nov 2008 03:35:46 -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/2008/Nov/13/t12265726545rf5kwun9aov01z.htm/, Retrieved Mon, 20 May 2024 11:18:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=24556, Retrieved Mon, 20 May 2024 11:18:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Testing Sample Mean with known Variance - Confidence Interval] [Pork quality test...] [2008-11-12 13:22:58] [7a664918911e34206ce9d0436dd7c1c8]
F RMPD    [Bivariate Kernel Density Estimation] [Various EDA topics] [2008-11-13 10:35:46] [c4248bbb85fa4e400deddbf50234dcae] [Current]
Feedback Forum
2008-11-16 14:17:20 [Ken Wright] [reply] 
Er is hier mooi 1 cluster die mooi op de regressierecht ligt. Dus de gegevens zijn niet te hard verspreid, want de regressierechte is eingelijk de beste rechte die je door je puntenwolk kan trekken. Maar ik denk dat tussen deze 2variabelen minder correlatie is als tussen de vorige 2 bivariate vergelijkingen, omdat hier de hoogtelijnen meer cirkelvormig zijn als bij de vorige.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
119,5
125
145
105,3
116,9
120,1
88,9
78,4
114,6
113,3
117
99,6
99,4
101,9
115,2
108,5
113,8
121
92,2
90,2
101,5
126,6
93,9
89,8
93,4
101,5
110,4
105,9
108,4
113,9
86,1
69,4
101,2
100,5
98
106,6
90,1
96,9
125,9
112
100
123,9
79,8
83,4
113,6
112,9
104
109,9
99
106,3
128,9
111,1
102,9
130
87
87,5
117,6
103,4
110,8
112,6
102,5
112,4
135,6
105,1
127,7
137
91
90,5
122,4
123,3
124,3
120
118,1
119
142,7
123,6
129,6
151,6
110,4
99,2
130,5
136,2
129,7
128
121,6
Dataseries Y:
Download CSV
Histogram 
98.6
98
106.8
96.6
100.1
107.7
91.5
97.8
107.4
117.5
105.6
97.4
99.5
98
104.3
100.6
101.1
103.9
96.9
95.5
108.4
117
103.8
100.8
110.6
104
112.6
107.3
98.9
109.8
104.9
102.2
123.9
124.9
112.7
121.9
100.6
104.3
120.4
107.5
102.9
125.6
107.5
108.8
128.4
121.1
119.5
128.7
108.7
105.5
119.8
111.3
110.6
120.1
97.5
107.7
127.3
117.2
119.8
116.2
111
112.4
130.6
109.1
118.8
123.9
101.6
112.8
128
129.6
125.8
119.5
115.7
113.6
129.7
112
116.8
127
112.1
114.2
121.1
131.6
125
120.4
117.7

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 1 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24556&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24556&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24556&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 time1 seconds
R Server'George Udny Yule' @ 72.249.76.132






Bandwidth
x axis7.99475369661637
y axis4.60713319379022
Correlation
correlation used in KDE0.591075502587546
correlation(x,y)0.591075502587546

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 7.99475369661637 \tabularnewline
y axis & 4.60713319379022 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.591075502587546 \tabularnewline
correlation(x,y) & 0.591075502587546 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24556&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]7.99475369661637[/C][/ROW]
[ROW][C]y axis[/C][C]4.60713319379022[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.591075502587546[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.591075502587546[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24556&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24556&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:

Bandwidth
x axis7.99475369661637
y axis4.60713319379022
Correlation
correlation used in KDE0.591075502587546
correlation(x,y)0.591075502587546


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 50 ; par2 = 50 ; par3 = 0 ; par4 = 0 ; par5 = 0 ; par6 = Y ; par7 = Y ;
Parameters (R input):
par1 = 50 ; par2 = 50 ; par3 = 0 ; par4 = 0 ; par5 = 0 ; par6 = Y ; par7 = Y ;
R code (references can be found in the software module):
par1 <- as(par1,'numeric')
par2 <- as(par2,'numeric')
par3 <- as(par3,'numeric')
par4 <- as(par4,'numeric')
par5 <- as(par5,'numeric')
library('GenKern')
if (par3==0) par3 <- dpik(x)
if (par4==0) par4 <- dpik(y)
if (par5==0) par5 <- cor(x,y)
if (par1 > 500) par1 <- 500
if (par2 > 500) par2 <- 500
bitmap(file='bidensity.png')
op <- KernSur(x,y, xgridsize=par1, ygridsize=par2, correlation=par5, xbandwidth=par3, ybandwidth=par4)
image(op$xords, op$yords, op$zden, col=terrain.colors(100), axes=TRUE,main=main,xlab=xlab,ylab=ylab)
if (par6=='Y') contour(op$xords, op$yords, op$zden, add=TRUE)
if (par7=='Y') points(x,y)
(r<-lm(y ~ x))
abline(r)
box()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Bandwidth',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'x axis',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'y axis',header=TRUE)
a<-table.element(a,par4)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'correlation used in KDE',header=TRUE)
a<-table.element(a,par5)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'correlation(x,y)',header=TRUE)
a<-table.element(a,cor(x,y))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')