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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 computationTue, 11 Nov 2008 09:58:31 -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/11/t1226422753nkm180tq7pne77q.htm/, Retrieved Sun, 19 May 2024 10:45:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23725, Retrieved Sun, 19 May 2024 10:45:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact131

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Bivariate Kernel Density Estimation] [Various EDA - Biv...] [2008-11-10 13:30:59] [4300be8b33fd3dcdacd2aa9800ceba23]
F    D    [Bivariate Kernel Density Estimation] [Q1] [2008-11-11 16:58:31] [708e5cce6cfef15b7edd0dea71956401] [Current]
Feedback Forum
2008-11-19 13:12:37 [Kristof Van Esbroeck] [reply] 
De student maakt gebruik van de juiste techniek, Bivariate Density, maar trekt echter geen conclusies.

Er werd een vergelijking gemaakt tussen de vervaardiging van producten van metaal en de vervaardiging van machines, apparaten en werktuigen.

De correlatie bedraagt 0.824488685204379, wat dus een positief verband weergeeft tussen de twee datareeksen. Hoe roder de zone is die we bekijken, hoe hoger de correlatie is op die plaats. Een groene zone duidt dan weer op een erg lage correlatie. De hoogtelijnen die we waarnemen verbinden de verschillende punten met eenzelfde dichtheid.

Je kan deze techniek slechts toepassen op 2 variabelen, maar het is wel erg handig om ze met elkaar te vergelijken. In tegenstelling tot een weergave in functie van de tijd.
2008-11-20 16:01:23 [Bas van Keken] [reply] 
De conclusie is dus dat er een dichtheid is van de data bij de correlatie. Verder kunnen er geen losse datagroepen worden waargenoemen.
2008-11-23 13:24:17 [Jonas De Kinder] [reply] 
Goede techniek maar geen conclusies getrokken. Er is te zien dat de data bij sterk gecorreleerd zijn (0.824488685204379. Dit valt ook af te leiden vanuit de grafiek. Hier zien we dat er zeer veel punten langs de rechte liggen en binnen hoogtelijnen rond de rechte.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101.6
101.2
111.6
109.4
105.4
119.6
87.7
93.8
115.6
121.3
104.9
103.9
95.2
102
117.4
111.3
109.6
123
88.8
98.8
119.9
122.1
115.5
107.1
99.3
102.5
111.2
109.7
109.8
124.4
85.6
95.4
115.1
116.2
120
109.9
104
104.3
120.2
112.5
122.3
130
94.8
103.9
128.8
137.6
130.8
125.2
119.1
120.4
136.6
129.8
135.8
151
105
117.3
144.6
154.6
137.3
129
125.3
Dataseries Y:
Download CSV
Histogram 
93.5
98.8
106.2
98.3
102.1
117.1
101.5
80.5
105.9
109.5
97.2
114.5
93.5
100.9
121.1
116.5
109.3
118.1
108.3
105.4
116.2
111.2
105.8
122.7
99.5
107.9
124.6
115
110.3
132.7
99.7
96.5
118.7
112.9
130.5
137.9
115
116.8
140.9
120.7
134.2
147.3
112.4
107.1
128.4
137.7
135
151
137.4
132.4
161.3
139.8
146
166.5
143.3
121
152.6
154.4
154.6
158
142.6

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=23725&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=23725&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23725&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 axis7.53354080123436
y axis7.85559164139053
Correlation
correlation used in KDE0.824488685204379
correlation(x,y)0.824488685204379

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 7.53354080123436 \tabularnewline
y axis & 7.85559164139053 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.824488685204379 \tabularnewline
correlation(x,y) & 0.824488685204379 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23725&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]7.53354080123436[/C][/ROW]
[ROW][C]y axis[/C][C]7.85559164139053[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.824488685204379[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.824488685204379[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23725&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23725&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 axis7.53354080123436
y axis7.85559164139053
Correlation
correlation used in KDE0.824488685204379
correlation(x,y)0.824488685204379


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')