FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_cloud.wasp
Title produced by softwareTrivariate Scatterplots
Date of computationFri, 07 Nov 2008 03:42:57 -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/07/t1226054703t35dip3qcg0cxcu.htm/, Retrieved Sun, 19 May 2024 05:57:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=22468, Retrieved Sun, 19 May 2024 05:57:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact288

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 topic...] [2008-11-07 10:38:01] [e5d91604aae608e98a8ea24759233f66]
F RMPD    [Trivariate Scatterplots] [Various EDA topic...] [2008-11-07 10:42:57] [55ca0ca4a201c9689dcf5fae352c92eb] [Current]
F RMPD      [Partial Correlation] [Various EDA topic...] [2008-11-07 10:48:55] [e5d91604aae608e98a8ea24759233f66]
F RMPD      [Box-Cox Linearity Plot] [Various EDA topic...] [2008-11-07 11:06:19] [e5d91604aae608e98a8ea24759233f66]
F RM D        [Box-Cox Normality Plot] [Various EDA topic...] [2008-11-10 11:55:49] [e5d91604aae608e98a8ea24759233f66]
F RMPD          [Maximum-likelihood Fitting - Normal Distribution] [Various EDA topic...] [2008-11-10 12:02:10] [e5d91604aae608e98a8ea24759233f66]
- RMPD          [Testing Variance - Critical Value (Region)] [Various types of ...] [2008-11-10 12:36:06] [e5d91604aae608e98a8ea24759233f66]
-   P             [Testing Variance - Critical Value (Region)] [Various types of ...] [2008-11-10 12:44:47] [e5d91604aae608e98a8ea24759233f66]
- RMPD            [Notched Boxplots] [Various types of ...] [2008-11-10 13:05:18] [e5d91604aae608e98a8ea24759233f66]
- RMPD          [Testing Variance - p-value (probability)] [Various types of ...] [2008-11-10 12:39:28] [e5d91604aae608e98a8ea24759233f66]
- RMPD      [Kendall tau Correlation Matrix] [Various EDA topic...] [2008-11-07 11:03:18] [e5d91604aae608e98a8ea24759233f66]
F RMPD      [Testing Mean with known Variance - Critical Value] [Case - Q1] [2008-11-07 11:22:19] [e5d91604aae608e98a8ea24759233f66]
F RM          [Testing Mean with known Variance - p-value] [Case - Q2] [2008-11-07 11:40:30] [e5d91604aae608e98a8ea24759233f66]
F RM          [Testing Mean with known Variance - Type II Error] [Case - Q3] [2008-11-07 11:49:06] [e5d91604aae608e98a8ea24759233f66]
F RM          [Testing Mean with known Variance - Sample Size] [Case - Q4] [2008-11-07 11:55:44] [e5d91604aae608e98a8ea24759233f66]
F RM          [Testing Population Mean with known Variance - Confidence Interval] [Case - Q5] [2008-11-07 12:04:51] [e5d91604aae608e98a8ea24759233f66]
- RM          [Testing Sample Mean with known Variance - Confidence Interval] [Case - Q6] [2008-11-07 12:10:38] [e5d91604aae608e98a8ea24759233f66]
F               [Testing Sample Mean with known Variance - Confidence Interval] [Case - Q6.] [2008-11-07 12:17:52] [e5d91604aae608e98a8ea24759233f66]
Feedback Forum
2008-11-17 18:28:31 [8e2cc0b2ef568da46d009b2f601285b2] [reply] 
De punten wolk is zeer moeilijk te lezen op een plat vlak. Om deze leesbaarder te maken zal men telkens een dimensie wegnemen dit kan je dan terugvinden in de matrix grafiek.
2008-11-22 17:59:02 [Kenny Simons] [reply] 
Bij de trivariate scatterplots moet je opletten met de kubussen, deze zorgen altijd voor vertekening omdat deze driedimensionaal zijn. Je kan namelijk niet goed zien hoe de afstanden tussen de punten zich verhouden. Hierdoor is het beter te gaan zien naar de tweedimensionale scatterplots. Hieruit kan je beter aflezen of er een relatie is tussen bepaalde tijdreeksen of niet.

Als ik de tweedimensionale scatterplots bekijk, zie ik dat er zo goed als geen correlatie is in de verschillende figuren. Ook in de Bivariate Kernel Density Plots zie ik zo goed als geen correlatie terug.

2008-11-23 14:15:01 [Chi-Kwong Man] [reply] 
Hier sluit ik me aan bij de mening van de vorige studenten. De trivariate scatterplot kan een vertekend beeld geven omdat het een 3-dimensioneel projectie op een plat vlak is waardoor men het moeilijk kan aflezen.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
99.29
98.69
107.92
101.03
97.55
103.02
94.08
94.12
115.08
116.48
103.42
112.51
95.55
97.53
119.26
100.94
97.73
115.25
92.8
99.2
118.69
110.12
110.26
112.9
102.17
99.38
116.1
103.77
101.81
113.74
89.67
99.5
122.89
108.61
114.37
110.5
104.08
103.64
121.61
101.14
115.97
120.12
95.97
105.01
124.68
123.89
123.61
114.76
108.75
106.09
123.17
106.16
115.18
120.6
109.48
114.44
121.44
129.48
124.32
112.59
Dataseries Y:
Download CSV
Histogram 
1.21
1.74
1.76
1.48
1.04
1.62
1.49
1.79
1.8
1.58
1.86
1.74
1.59
1.26
1.13
1.92
2.61
2.26
2.41
2.26
2.03
2.86
2.55
2.27
2.26
2.57
3.07
2.76
2.51
2.87
3.14
3.11
3.16
2.47
2.57
2.89
2.63
2.38
1.69
1.96
2.19
1.87
1.6
1.63
1.22
1.21
1.49
1.64
1.66
1.77
1.82
1.78
1.28
1.29
1.37
1.12
1.51
2.24
2.94
3.09
Dataseries Z:
Download CSV
Histogram 
1946,81
1765,9
1635,25
1833,42
1910,43
1959,67
1969,6
2061,41
2093,48
2120,88
2174,56
2196,72
2350,44
2440,25
2408,64
2472,81
2407,6
2454,62
2448,05
2497,84
2645,64
2756,76
2849,27
2921,44
3080,58
3106,22
3119,31
3061,26
3097,31
3161,69
3257,16
3277,01
3295,32
3363,99
3494,17
3667,03
3813,06
3917,96
3895,51
3733,22
3801,06
3570,12
3701,61
3862,27
3970,1
4138,52
4199,75
4290,89
4443,91
4502,64
4356,98
4591,27
4696,96
4621,4
4562,84
4202,52
4296,49
4435,23
4105,18
4116,68

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22468&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]9 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=22468&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22468&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 time9 seconds
R Server'George Udny Yule' @ 72.249.76.132


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 50 ; par2 = 50 ; par3 = Y ; par4 = Y ; par5 = Omzet consumptiegoederen ; par6 = Inflatiecijfer ; par7 = Aandelen BEL20 ;
Parameters (R input):
par1 = 50 ; par2 = 50 ; par3 = Y ; par4 = Y ; par5 = Omzet consumptiegoederen ; par6 = Inflatiecijfer ; par7 = Aandelen BEL20 ;
R code (references can be found in the software module):
x <- array(x,dim=c(length(x),1))
colnames(x) <- par5
y <- array(y,dim=c(length(y),1))
colnames(y) <- par6
z <- array(z,dim=c(length(z),1))
colnames(z) <- par7
d <- data.frame(cbind(z,y,x))
colnames(d) <- list(par7,par6,par5)
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
if (par1>500) par1 <- 500
if (par2>500) par2 <- 500
if (par1<10) par1 <- 10
if (par2<10) par2 <- 10
library(GenKern)
library(lattice)
panel.hist <- function(x, ...)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='black', ...)
}
bitmap(file='cloud1.png')
cloud(z~x*y, screen = list(x=-45, y=45, z=35),xlab=par5,ylab=par6,zlab=par7)
dev.off()
bitmap(file='cloud2.png')
cloud(z~x*y, screen = list(x=35, y=45, z=25),xlab=par5,ylab=par6,zlab=par7)
dev.off()
bitmap(file='cloud3.png')
cloud(z~x*y, screen = list(x=35, y=-25, z=90),xlab=par5,ylab=par6,zlab=par7)
dev.off()
bitmap(file='pairs.png')
pairs(d,diag.panel=panel.hist)
dev.off()
x <- as.vector(x)
y <- as.vector(y)
z <- as.vector(z)
bitmap(file='bidensity1.png')
op <- KernSur(x,y, xgridsize=par1, ygridsize=par2, correlation=cor(x,y), xbandwidth=dpik(x), ybandwidth=dpik(y))
image(op$xords, op$yords, op$zden, col=terrain.colors(100), axes=TRUE,main='Bivariate Kernel Density Plot (x,y)',xlab=par5,ylab=par6)
if (par3=='Y') contour(op$xords, op$yords, op$zden, add=TRUE)
if (par4=='Y') points(x,y)
(r<-lm(y ~ x))
abline(r)
box()
dev.off()
bitmap(file='bidensity2.png')
op <- KernSur(y,z, xgridsize=par1, ygridsize=par2, correlation=cor(y,z), xbandwidth=dpik(y), ybandwidth=dpik(z))
op
image(op$xords, op$yords, op$zden, col=terrain.colors(100), axes=TRUE,main='Bivariate Kernel Density Plot (y,z)',xlab=par6,ylab=par7)
if (par3=='Y') contour(op$xords, op$yords, op$zden, add=TRUE)
if (par4=='Y') points(y,z)
(r<-lm(z ~ y))
abline(r)
box()
dev.off()
bitmap(file='bidensity3.png')
op <- KernSur(x,z, xgridsize=par1, ygridsize=par2, correlation=cor(x,z), xbandwidth=dpik(x), ybandwidth=dpik(z))
op
image(op$xords, op$yords, op$zden, col=terrain.colors(100), axes=TRUE,main='Bivariate Kernel Density Plot (x,z)',xlab=par5,ylab=par7)
if (par3=='Y') contour(op$xords, op$yords, op$zden, add=TRUE)
if (par4=='Y') points(x,z)
(r<-lm(z ~ x))
abline(r)
box()
dev.off()