• Journal of Distribution Science
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• v.12 no.9
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• pp.47-52
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• 2014

Purpose - There has been little research on the variables influencing studio apartment values. This study aims to identify variables affecting the value of studio apartments in Seoul by empirically examining the interaction between sale prices and characteristics studio apartment characteristics. Research design, data, and methodology - We have analyzed data pertaining to 142 studio apartments in September 2010. A regression analysis model is constructed to test the significance of the variables in relation to the studio apartment sale prices per m2 in Seoul. Results - The age of the building is comparatively more significant than land use as the explanatory variable. Land price is the key variable affecting studio apartment sale prices and investors are willing to pay high implicit sale prices for locations that are associated with high land prices. Conclusions - The age of buildings explains a significant portion of the variability of the sale prices of studio apartment. Higher land prices result in higher sale prices for studio apartments. The older the buildings, the lower the sale prices of the studio apartments.

• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.511-521
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• 2020

In this paper, a partially linear multivariate model with error in the explanatory variable of the nonparametric part, and an m dimensional response variable is considered. Using the uniform consistency results found for the estimator of the nonparametric part, we derive an estimator of the parametric part. The dependence of the convergence rates on the errors distributions is examined and demonstrated that proposed estimator is asymptotically normal. In main results, both ordinary and super smooth error distributions are considered. Moreover, the derived estimators are applied to the economic behaviors of consumers. Our method handles contaminated data is founded more effectively than the semiparametric method ignores measurement errors.

• Journal of the Korean Home Economics Association
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• v.37 no.5
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• pp.31-48
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• 1999

This research was to assess the high school girls' need about the computer assisted instruction(CAI) in the Home Economics Curriculum. In One-way ANOVA, the high school girls' need about the CAI differs in the educational level of the father and the mother, the preference for the Home Economics, the involvement with the Home Economics and the preference for a teaching method of Home Economics. MCA was done to assess the independent explanatory power of predictory variables. The educational level of father and mother were included separately in different model. The MCA that the educational level of father was included in, The most influential variable was the preference for the Home Economics and the involvement with Home Economics was the second. The MCA that the educational level of mother was included in, The most influential variable was the preference for the Home Economics and the educational level of mother was the second.

• Asian Journal of Business Environment
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• v.12 no.4
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• pp.25-34
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• 2022

Purpose This research develops the expanded Technology Acceptance Model (TAM) to investigate the relationship between perceived usefulness and perceived ease of use with satisfaction and loyalty by considering the role of sharia compliance, commitment, and trust. Research design, data and methodology: A data source is 300 respondents from a self-administered survey. The target population is Muslims who are customers of Islamic banks, with age at least 18 years, and used e-banking to make payment transactions. The analysis methods are MANOVA and Multiple Linear Regression. Results: The results suggest that intention to use and actual behavior variables are replaced with satisfaction and loyalty. Commitment is not recommended, while trust is an explanatory variable that can be used as an external variable. Conclusions: It is important to increase satisfaction and commitment also concentrate to various aspects of sharia compliance to increase customer's loyalty to use e-banking. The fulfillment of sharia compliance by Islamic banks will increase the Islamic bank customers loyalty.

• The Korean Journal of Applied Statistics
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• v.10 no.2
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• pp.293-308
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• 1997

Sliced inverse regression is a method for reducing the dimension of the explanatory variable X without going through any parametric or nonparametric model fitting process. This method explores the simplicity of the inverse view of regression; that is, instead of regressing the univariate output varable y against the multivariate X, we regress X against y. In this article, we propose bivariate sliced inverse regression, whose method regress the multivariate X against the bivariate output variables $y_1, Y_2$. Bivariate sliced inverse regression estimates the e.d.r. directions of satisfying two generalized regression model simultaneously. For the application of bivariate sliced inverse regression, we decompose the output variable y into two variables, one variable y gained by projecting the output variable y onto the column space of X and the other variable r through projecting the output variable y onto the space orthogonal to the column space of X, respectively and then estimate the e.d.r. directions of the generalized regression model by utilize two variables simultaneously. As a result, bivariate sliced inverse regression of considering the variable y and r simultaneously estimates the e.d.r. directions efficiently and steadily when the regression model is linear, quadratic and nonlinear, respectively.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.365-377
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• 2020

We discuss latent factor regression when constructing a common structure inherent among explanatory variables to solve multicollinearity and use them as regressors to construct a linear model of a response variable. Bayesian estimation with LASSO prior of a large penalty parameter to construct a significant factor loading matrix of intrinsic interests among infinite latent structures. The estimated factor loading matrix with estimated other parameters can be inversely transformed into linear parameters of each explanatory variable and used as prediction models for new observations. We apply the proposed method to Product Service Management data of HBAT and observe that the proposed method constructs the same factors of general common factor analysis for the fixed number of factors. The calculated MSE of predicted values of Bayesian latent factor regression model is also smaller than the common factor regression model.

• Journal of the Korean Home Economics Association
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• v.34 no.4
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• pp.86-101
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• 1996

The purpose of the study was to investigate whether there were differences between the Paternal child rearing Involvement and the Parental Satisfaction according to child's sex, father's age and the birth order of child. The subjects surveyed were 271 fathers 132 in their thiries and 139 in their forties who live in Kwang-ju. And the children considered are 128 boys and 143 girls. Among them, first-born children are 143 members, second-born are 103, and third-born are 25. Factor analysis, frequencies, mean, standard deviation, Cronbach's α, one way-ANOVA, Pearson's correlation coefficient, and step-wise regression are used for data-analysis. The main results were as follows : (1) There were some significant differences in the Paternal child rearing Involvement according to the child's sex, while there was no difference as related the father's age and the birth-order of child. (2) The were some significant differences in the father's Parental Satisfaction which is involved child's sex and the father's age, but there was no difference as to the birth-order of child. (3) There were some significant differences between the Paternal Child rearing Involvement and the Parental Satisfaction, and between its subfactor and the Parental Satisfaction, too. (4) The result of the step-wise regression, which analyses the Paternal child rearing Involvement and the background variables as to father's Parental Satisfaction, shows the Parent-child relationship variable (accounted for about32% of the general variation), spouse support, support of children, general satisfaction, and parent's role conflict at intensity in order. Of the above mentioned five fields, house-activities were the first factor in determining this order. And the personal interaction plays an important role in fulfilling general satisfaction and the support of children. The leisure-action factor was the second explanatory factor in establishing the parent-child relationship. Finally father's age was the fourth explanatory factor in assessing the parent-child relationship variable considering the background variables.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.5
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• pp.259-268
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• 2020

This study aims to provide empirical evidence on the causal relationship between bribery and firm innovation. To this end, we use a micro-dataset of small and medium firms in Vietnam surveyed in 2015. Given the binary nature of the dependent variable, a simple probit regression model is employed. However, as bribery variable is potentially endogenous, a simple probit regression may give biased estimates. We deal with the potential endogeneity by making use of the bivariate probit model. A property of the bivariate probit model is that it can produce efficient estimates of a typical probit model with endogenous binary explanatory variable. A Hausman-like likelihood ratio test is implemented following the estimation to test the existence of endogeneity. We find that bribery significantly undermines firm innovation. Also, firms run by household appear less innovative. The probability of innovation diminishes significantly if firm owners or managers have previous experience in firm products. As expected, larger firms seem to be more innovative. Exporters tend to be more innovative compared to non-exporters. Our findings provide support to the hypothesis that bribery is detrimental to firm innovation and, thus, innovation may be a mediating channel, through which, bribery impedes firm long-term performance.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.937-944
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• 2021

The problem of selecting variables is addressed in partial linear regression. Model selection for partial linear models is not easy since it involves nonparametric estimation such as smoothing parameter selection and estimation for linear explanatory variables. In this work, several approaches for variable selection are proposed using a fast forward selection algorithm, least angle regression (LARS). The proposed procedures use t-test, all possible regressions comparisons or stepwise selection process with variables selected by LARS. An example based on real data and a simulation study on the performance of the suggested procedures are presented.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.7
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• pp.27-39
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• 2022

The exchange rate is an important macroeconomic variable that influences internal and external balances. Nepal follows a dual exchange rate such that the Nepali rupee (NPR) is pegged with the Indian rupee (INR) but floats with the United States dollar (USD) and all other currencies. There have been very few studies on the exchange rate of Nepal, of which the majority focus on the bivariate relationship between exchange rate and another variable. However, this paper analyses the multivariate relationship between the USD-NPR exchange rate and major macroeconomic variables. Determinants of Nepal's exchange rate have been derived with multiple regression using the ordinary least square (OLS) approach. Since the explanatory variables could not significantly capture the movement of the dependent variable, a long-run relationship between Nepal and India's exchange rate has been analyzed using Engle-Granger cointegration to establish a relationship as suggested by a graphical representation. This explains that Nepal's exchange rate long run is determined by India's exchange rate than its own fundamentals. In addition, the macro-linkages of Nepal's macroeconomic variables have been analyzed using Standard Vector Autoregressive models followed by impulse response analysis which is useful for policy decisions. Some policy implications indicating the sustainability of Nepal's pegged regime have been drawn based on the empirical analysis.