• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.917-925
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• 2010

The canonical correlation biplot is a 2-dimensional plot for graphically investigating the relationship between two sets of variables and the relationship between observations and variables in the canonical correlation analysis. Recently, Choi and Choi (2008) suggested a method for investigating the relationship between skill and competition score factors of KLPGA players using this biplot. Choi et al. (2010) used this biplot to analyze the player characteristic factors and competitive factors of tennis Grand Slam competition. Moreover, Huh (1999) provided a generalized canonical correlation analysis and biplot for more than three sets of variables. A Procrustes analysis is a useful tool for comparing shapes between configurations. This study will provide a method to investigate the relationship between physique, physical fitness and basic skill factors of tennis players in the Korea Tennis Association using a generalized canonical correlation biplot and Procrustes analysis.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.429-439
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• 2008

Canonical correlation biplot is 2-dimensional plot for investigating the relationship between two sets of variables and the relationship between observations and variables in canonical correlation analysis graphically. In general, biplot is useful for giving a graphical description of the data. However, this general biplot and also canonical correlation biplot do not give some concise interpretations between variables and observations when the number of observations are large. Recently, for overcoming this problem, Choi and Kim (2008) suggested a method to interpret the biplot analysis by applying the K-means clustering analysis. Therefore, in this study, we will apply their method for investigating the relationship between skill and competition score factors of KLPGA players using canonical correlation biplot and cluster analysis.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.521-529
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• 2012

Simple canonical correlation biplot is a graphical method to investigate two sets of variables and observations in simple canonical correlation analysis. If we consider the set of covariate variables that linearly affects two sets of variables, we can apply the partial canonical correlation biplot in partial canonical correlation analysis that removes the linear effect of the set of covariate variables on two sets of variables. On the other hand, we consider the set of covariate variables that linearly affect one set of variables but not the other. In this case, if we apply the simple or partial canonical correlation biplot, we cannot clearly interpret other two sets of variables. Therefore, in this study, we will apply the semi-partial canonical correlation analysis of Timm (2002) and remove the linear effect of the set of covariate variables on one set of variables but not the other. And we suggest the semi-partial canonical correlation biplot for interpreting the semi-partial canonical correlation analysis. In addition, we will compare shapes and shape the variabilities of the simple, partial and semi-partial canonical correlation biplots using a procrustes analysis.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.31-41
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• 2014

Biplot is a useful graphical method to simultaneously explore the rows and columns of a two-way data matrix. In particular, principal component factor biplot is a graphical method to describe the interrelationship among many variables in terms of a few underlying but unobservable random variables called factors. If we consider the unobservable variables (which are mutually independent and also non-Gaussian), we can apply the independent component analysis decomposing a mixture of non-Gaussian in its independent components. In this case, if we apply the principal component factor analysis, we cannot clearly describe the interrelationship among many variables. Therefore, in this study, we apply the independent component analysis of Jutten and Herault (1991) decomposing a mixture of non-Gaussian in its independent components. We suggest an independent component biplot to interpret the independent component analysis graphically.

• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.559-566
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• 2011

Biplot is a useful graphical method to explore simultaneously rows and columns of two-way data matrix. In particular, canonical correlation biplot is a method for investigating two sets of variables and observations in canonical correlation analysis graphically. For more than three sets of variables, we can apply the generalized canonical correlation biplot in generalized canonical correlation analysis which is an expansion of the canonical correlation analysis. On the other hand, we consider the set of covariate variables which is affecting the linearly two sets of variables. In this case, if we apply the generalized canonical correlation biplot, we cannot clearly interpret the other two sets of variables due to the effect of the set of covariate variables. Therefor, in this paper, we will apply the partial canonical correlation analysis of Rao (1969) removing the linear effect of the set of covariate variables on two sets of variables. We will suggest the partial canonical correlation biplot for inpreting the partial canonical correlation analysis graphically.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.451-459
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• 1998

The analysis of data from a multiresponse experiment requires careful consideration of the multivariate nature of the data. In a multiresponse sitation, the optimization problem is more complex than in the single response case. The biplot is a graphical tool which make the analyst to understand the correlation of the response variables, the relation of the response variables arid the explanatory variables and the relative importance of the explanatory variables. In case of good fitting of the first order model, we can draw the biplot with the first order experimental design. Otherwise, we can make the biplot with the second order experimental design by adding other experimental points.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.855-864
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• 2009

Canonical correlation biplot is 2-dimensional plot for investigating the relationship between two sets of variables and the relationship between observations and variables in canonical correlation analysis graphically. Recently, Choi and Choi (2008) suggested a method for investigating the relationship between skill and competition score factors of KLPGA players using canonical correlation biplot and cluster analysis. analysis. Procrustes analysis is very useful tool for comparing shape between configurations. Therefore, in this study, we will provide a method for investigating the relationship between player characteristic factors and competitive factors of tennis grand slams competition using Canonical correlation biplot and Procrustes analysis.

• The Korean Journal of Applied Statistics
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• v.9 no.2
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• pp.55-72
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• 1996

It is essential in modern industry that quality and procuctivity are improved continuously. To accomplish this purpose, quality control must be maintained in all parts of a company. Recently, some tire manufacture companies are beginning to show interest in quality control. They have tried to achive some results through the statistical analysis for the experimental data which has accumulated up to now and then they strive to determine the structural relationship between the design factors in tire construction and tire performance characteristics. The measurement data obtained from the construction engineering is given in multivariate form owing to the various properties found in tire design components as wll as in performance. Also it may be existed the relationship among the multimple response variables. Thus we proposes the use of the biplot graphical display as an analytic tool of data matrices with complex respects. The proposed biplots are also availalbe to understand both the underlying structure of the data and the roles played by the different components. In particular, we consider the matter of how best to use the biplots in the maltivariate analysis of variance and multiple response data. Finally we apply this method to analyze the actual data.

• Communications for Statistical Applications and Methods
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• v.20 no.6
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• pp.491-498
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• 2013

We consider support vector machines(SVM) to predict Y with p numerical variables $X_1$, ${\ldots}$, $X_p$. This paper aims to build a biplot of p explanatory variables, in which the first dimension indicates the direction of SVM classification and/or regression fits. We use the geometric scheme of kernel principal component analysis adapted to map n observations on the two-dimensional projection plane of which one axis is determined by a SVM model a priori.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.933-941
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• 2010

Biplot is a modern graphical methodology allowing for the projection of high-dimensional data to a low-dimensional subspace that is rich in information on variation in the data, correlation among variables as well as class separation. For the construction of biplots, we use a BiplotGUI package in a free statistical software R with increasing popularity. Moreover, using data from questionnaires given to Chonbuk National University freshmen in 2009, the relationship between career goals and career maturity are studied by applying the biplot method.