• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.331-338
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• 2006

Quantification method III is designed by C. Hayashi as visualizing technique for two-way cross-classified tables. Specially in Japan, its usefulness is timely proven in social and marketing surveys. In several instances, relatively large quantification scores are assigned to low-frequency categories. Thus, they lead to unreliable data interpretation. The aim of this study is to develop stable solution to overcome such traits of quantification method III. The solution is of shrinkage type induced by small perturbations and is applied to a multiple response data obtained in a Korean social survey.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.421-428
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• 2009

On the visualization of categorical data, if the number of categories is small, we can consider Hayashi Quantification Method 3 for visualization of the categories of the variables. But it is known that the method is unstable because it quantifies more significantly for the small frequency categories rather than large frequency categories. The purpose of this research is to propose the visualization of large two-way crosstabulation data by PLS methods for checking the relationship between the categories of row and column variables. In this research, we utilize the PLS visualization methods (Huh et al., 2007) that is proposed for visualization of the qualitative data to visualize the categories of the large categorical data. We also compared both methods by applying them to real data, and studied the results from PLS visualization method on the real categorized data with many categories.

• Korean Journal of Soil Science and Fertilizer
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• v.12 no.1
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• pp.15-23
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• 1979

In the first paper of the series the five soil fertility factors were evaluated by means of principal component analysis and varimax method. They are interpreted as representing, 1) skeletal available phosporus status, 2) organnic matter status, 3) salt status 4) base status, and 5) free oxide status. In order to resynthesize such fragmented information for the overall soil fertility evaluation, the method of multiple regression analysis was adopted, using the five factor scores and yield data for Korean paddy soils as independent and dependent variables respectively. As test of linear models with different combinations of independent variables the results of t-test of regression coefficient were revealed that the organic matter status (FII) has no relevance to the yield of paddy and that the free oxides and salt supply has by it self only an insignificant contribution to the yield. The multiple correlation coefficient (R) revealed its multiple regression analysis was as low as 0.43. Introduction of quadratic terms to the linear model bettered the result. Thus multiple correlation coefficient (R) was increased as 0.59. Therefore, a coefficient of determination 0.35 was obtained by a quadratic model with interaction terms among the five fertility constituents. Generally we think that the fertility factor has more contribution to raise the rice yield in paddy and that the failure of yield prediction by fertility factor scores was caused by one of follows; 1) the roughness of the yield inspection, and 2) missextraction of fertility constituents. The second step in this study, assuming that the residuals by multiple regression analysis were due to factors other than soil fertility, we can now proceed to predicting the yield from the field characters with the classified fertility groups by means of Hayashi's theory of quantification No. 1. Such variables as fertility groups (FTYG), water availability (WATER), soil drainage (DRNG), climatic zone (CLIZ), surface soil's stickiness (STCKT), surface soil's dry consistence (DCNST), and surface soil's texture (FTEXT) are taken up as the explanatory variables. The quantification appears reasonable; the well to extremely well in soil drainage, very sticky of surface soil, inefficiency in water availability, coarse texture, and very hard to extremely hard dry consistence in soil are detrimental to the rice yield. The R was as high as 0.90 for the set of variables. But the given explanatory variables in this study were not quite effective in explaining rice yield. The method developed seems to be promising only if properly collected data are available. Conditions that should be satisfied in the yield inspection obtained from common cultivator for the purpose of deriving a prediction equation were put forward.