• Journal of Korean Academy of Nursing
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• v.23 no.1
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• pp.130-141
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• 1993

This study was to find out variables influenced on the medication patterns (voluntary medication, in-voluntary medication) of antipsychotics taken by schizophrenic outpatients. Purposes of this study were to be identified that there was the significant difference between the group of voluntary medication and involuntary, and that which variables had infuence on outpsychotics medication. The sample consisted of 30 patients takeing their pills voluntary (voluntary medication group), and 15 patients involuntary(involuntary medication group) at a psychiatry hospital and a psychiatric unit of a The findings of the study are as follows : university hospital in Daegu. Data were collected from September to October, 1991 through interview using questionare about antipsychotics medication. Data were analyzed by the technique of two group discriminant function analysis using SPSS pc$^{+}$ 1) Discriminant function discriminating between voluntary medication and involuntary medication was significant at the level of 10% significance (sig.=.055, p〈.10) Hit-ratio was very high (91. 1％) 2) One of General variables, SEX, was significant of discriminating between two medication patterns at the level of 10% significance. 3) One of Family Environmental Variables, BROTH(a number of brother), were significant between two medication patterns. (p〈.10) 4) One of Therapeutic Environmental Variables, FAMHX, was significant between two medication patterns at the level of 10% significance. 5) One of Variables Related to Drug and Medication, NECESS, was significant between two medication patterns. (p〈.05) 6) Variables Related to Disease was not significant between two medication patterns.s.

• 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.

• Communications of the Korean Mathematical Society
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• v.28 no.1
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• pp.87-105
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• 2013

The aim of the present paper is to study some generating functions of generalized Bateman's and Pasternak's polynomials of two variables.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.631-652
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• 2023

In the two-condition within-subject mediation design, pairs of variables such as mediator and outcome are observed under two treatment conditions. The main objective of the design is to investigate the indirect effects of the condition difference (sum) on the outcome difference (sum) through the mediator difference (sum) for comparison of two treatment conditions. The natural condition variables mean the original variables, while the rotated condition variables mean the difference and the sum of two natural variables. The outcome difference (sum) is expressed as a linear model regressed on two natural (rotated) mediators as a parallel two-mediator design in two condition approaches: the natural condition approach uses regressors as the natural condition variables, while the rotated condition approach uses regressors as the rotated condition variables. In each condition approach, the total indirect effect on the outcome difference (sum) can be expressed as the sum of two individual indirect effects: within- and cross-condition indirect effects. The total indirect effects on the outcome difference (sum) for both condition approaches are the same. The invariance of the total indirect effect makes it possible to analyze the nature of two pairs of individual indirect effects induced from the natural conditions and the rotated conditions. The two-condition within-subject design is extended to the addition of a between-subject moderator. Probing of the conditional indirect effects given the moderator values is implemented by plotting the bootstrap confidence intervals of indirect effects against the moderator values. The expected indirect effect with respect to the moderator is derived to provide the overall effect of moderator on the indirect effect. The model coefficients are estimated by the structural equation modeling approach and their statistical significance is tested using the bias-corrected bootstrap confidence intervals. All procedures are evaluated using function lavaan() of package {lavaan} in R.

• Journal of Family Resource Management and Policy Review
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• v.9 no.1
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• pp.113-130
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• 2005

The purpose of this study was to show the expected age of retirement of the couple, husband and wife, individually, the difference between the expected age of retirement of the husband and that of the wife, and to analyze contributing variables. The sample in this study numbered 517, of which 229 were husbands and 288 were wives. The independent variables were divided into three factors including personal, financial, and work related variables. The results of this study were as follows. Among two earner households, both husbands and wives expected the husband's age of retirement to be higher than of the wife. The difference between the husband's expected age of retirement and the wife's expected age of retirement was five years, on average. For the husband, personal, financial, and work related variables had effects on his expected age of retirement. However, for the wife it was only the perception of future work life and expected age of retirement of the partner which had very significant effects statistically. The variables affecting the difference between the expected age of retirement of the husband and that of the wife included the personal variables as well as interaction of the couple related variables. The results showed that first of all, the planning of retirement among two earner households needs to be focused on the couple rather than on the individual.

• Journal of Korean Society for Quality Management
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• v.28 no.3
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• pp.155-164
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• 2000

This paper considers the problem of determining the optimum proccss mean value of the quality characteristic of interest, and the screening limit for two correlated variables under single-stage screening. In the single-stage screening, inspection is performed on two correlated variables which are correlated with the quality characteristic of interest. Model is constructed which involves selling price, production, inspection, and penalty costs. Method for finding the optimum process mean and screening limit are presented when the quality characteristic of interest and the correlated variables are assumed to be jointly normally distributed. A numerical example is presented and numerical analysis is performed to compare the proposed screening based on two screening variables with screening based on one screening variable.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.577-587
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• 2023

Conventional categorical data imputation techniques, such as mode imputation, often encounter issues related to overestimation. If the variable has too many categories, multinomial logistic regression imputation method may be impossible due to computational limitations. To rectify these limitations, we propose a two-stage imputation method. During the first stage, we utilize the Boruta variable selection method on the complete dataset to identify significant variables for the target categorical variable. Then, in the second stage, we use the important variables for the target categorical variable for logistic regression to impute missing data in binary variables, polytomous regression to impute missing data in categorical variables, and predictive mean matching to impute missing data in quantitative variables. Through analysis of both asymmetric and non-normal simulated and real data, we demonstrate that the two-stage imputation method outperforms imputation methods lacking variable selection, as evidenced by accuracy measures. During the analysis of real survey data, we also demonstrate that our suggested two-stage imputation method surpasses the current imputation approach in terms of accuracy.

• Journal of Korean Society for Quality Management
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• v.47 no.2
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• pp.271-281
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• 2019

Purpose: The purpose of this study is to suggest a more efficient distance measure taking into account the relationship between categorical variables for categorical data cluster analysis. Methods: In this study, the association-based dissimilarity was employed to calculate the distance between two categorical data observations and the distance obtained from the association-based dissimilarity was applied to the PAM cluster algorithms to verify its effectiveness. The strength of association between two different categorical variables can be calculated using a mixture of dissimilarities between the conditional probability distributions of other categorical variables, given these two categorical values. In particular, this method is suitable for datasets whose categorical variables are highly correlated. Results: The simulation results using several real life data showed that the proposed distance which considered relationships among the categorical variables generally yielded better clustering performance than the Hamming distance. In addition, as the number of correlated variables was increasing, the difference in the performance of the two clustering methods based on different distance measures became statistically more significant. Conclusion: This study revealed that the adoption of the relationship between categorical variables using our proposed method positively affected the results of cluster analysis.

• 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.

• Kyungpook Mathematical Journal
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• v.18 no.2
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• pp.311-319
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• 1978

This paper deals with the evaluation of two definite integrals involving spheroidal wave function, H-function of two variables, and the generalized hypergeometric function. Also, an expansion formula for the product of generalized hypergeometric function and the H-function of two variables has been obtained. Since the H-function of two variables, spheroidal wave functions, and the generalized hypergeometric function may be transformed into a number of higher transcendental functions and polynomials, the results obtained in this paper include some known results as their particular cases. As an application of such results, a problem of heat conduction in a non-homogenous bar has been solved by using the generalized Legendre transform [9].