• Journal of the Korean Home Economics Association
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• v.39 no.6
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• pp.61-78
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• 2001

The objectives of this study were to examine the propensity of job related variables, self-esteem, overall job satisfaction, satisfaction about six categories in the job(task, boss, payment, co-workers, promotion, job environment) of life insurance planners, to investigate the effects of self-esteem, demographic variables, job characteristics variables on the overall job satisfaction and the satisfaction of six categories in the job. The survey of this study was conducted by means of self-administered questionnaire with 275 life insurance planners located in Taegu. Major findings were as follows:(1) The propensity of self-esteem and overall job satisfaction of life insurance planners averages 3.75 and 3.35 points(5 Likert scale). The propensity of satisfaction about task, boss, payment, co-workers, promotion, job environment of life insurance planners averages 4.22, 2.67, 1.68, 2.09, 1.71, 2.65 points separately(5 Likert scale). (2) According to the results for examining the relative influences of variables affecting overall satisfaction of life insurance planners, the relative importance of related variables are in the order of , self-esteem in the job, social dignity of the job, the prospect about the dignity of life insurance planner, the motive of having job. Explanatory power of these variables totaled 43.5%.

• Journal of the Korean Society of Tobacco Science
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• v.27 no.1 s.53
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• pp.141-152
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• 2005

This study focused on the relationships among leaf and smoke components and sensory properties following tobacco leaf blending. A completely randomized experimental design was used to evaluate components of leaf and smoke and sensory properties for sample cigarettes with four mixtures of flue cured and burley tobacco (40:60, 60:40, 80:20 and 100:0). Eleven leaf components, six smoke components, and eight sensory properties of smoking taste were analyzed. A sensory evaluation method known as quantitative descriptive analysis was used to evaluate perceptual strength on a fifteen score scale. Raw data from ten trained panelists were obtained and statistically analyzed. Based on the MANOVA, clustering analysis, correlation matrix and partial least square (PLS) method were applied to find out which smoke component most affected sensory properties. The PLS method was used to remove the influence between explanatory variables in the leaf, smoke components derived from the results. High correlations (p<0.0l) were found among ten specific leaf and smoke components and sensory attributes. Total nitrogen, ammonia, total volatile base, and nitrate in the leaf were significantly correlated (p<0.05) with impact, bitterness, tobacco taste, irritation, smoke volume, and smoke pungency. From the results of PLS analysis, influence variables are used to explain about the correlation. In terms of bitterness, with only two explanatory variables, Leaf $NO_3$ and Leaf crude fiber were enough for guessing their correlation. In the distance weighted least square fitting analysis, carbon monoxide highly influenced bitterness, hay like taste, and smoke volume.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.3
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• pp.49-56
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• 2010

The Genetic Algorithm is one of the population surface modelling tool in the field of urban and environmental research based on the gridded population data. Taking the East-Hwasung area as the case, this study first builds a gridded population data based on the GIS databases as well as municipal population survey data. The study then constructs the attribute values of the explanatory variables by way of GIS tools. The regression model constructed with the same variables is also run as a comparative purpose at the same time. It is shown that the GenAlg output predicted as much consistent and meaningful coefficient estimates for the explanatory variables as the regression model, indicating that it is a very useful interdisciplinary research tool to find optimal solutions in urban problems.

• Journal of Preventive Medicine and Public Health
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• v.42 no.5
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• pp.305-314
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• 2009

Objectives : The physical activity of Korean adolescents and its distribution based on social characteristics have not yet been fully assessed. This study intends to reveal the distribution of physical activity by its subgroups and offer possible explanatory variables. Methods : The 3rd Korea Youth Risk Behavior Web-based Survey was analyzed for this study. The appropriateness of physical activity was defined by Korea s Health Plan 2010 and physical inactivity was assessed independently. Family affluence scale, parents education levels, subjective economic status, grade, and school location were considered explanatory variables. All statistical analysis was conducted using SAS ver. 9.1. Results : The proportion of participants engaging in vigorous physical activity was high in males (41.6%), at a low grade (38.5%), within the high family affluence scale group (35.5%). The distribution of participants engaging in moderate physical activity showed similar patterns, but the overall proportion was lower (9.8%). Low family affluence and students with lower subjective economic status reported a higher prevalence of physical inactivity. In multiple logistic regression analysis for physical activity, significant factors included family affluence scale (p<0.05). For physical inactivity, family affluence scale, parents education levels, and subjective economic status were included as significant factors (p<0.05). Conclusions : The results suggest that the physical activity and inactivity of adolescents may be affected by socioeconomic variables, such as family affluence scale. This implies the need to take proper measures to address these socio-economic inequalities.

• The Journal of Society for e-Business Studies
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• v.25 no.1
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• pp.177-202
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• 2020

We propose the statistical modeling and empirical results that can be utilized to identify and interpret the structural factors of international arms exports in recent years. The building blocks of research comprise the following questions; which would be the explanatory variables for the changing trend of international arms exports, whether the statistical significance can be verified on those variables and how those are interpreted for the future policy making purpose. We use the dataset of top 40 countries from SIPRI's Arms Transfers Database and analyze several regression models which consist of explanatory variables derived from research hypotheses. The most noticeable result is that the national fiscal reserve is shown to have consistent influence on the arms exports changes. UN security council members' group also has dominant power to make a formation of arms exports market block. Furthermore, gross domestic product and net exports volume in the national economy would seem to be related to changes of international arms exports in post-2000 period as well.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.87-100
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• 2003

Most of statistical data are generated by a set of dynamic, stochastic, and simultaneous relations. An important question is how to specify statistical models so that they are consistent with the dynamic feature of those data. A general hypothesis is that the lagged effect of a change in an explanatory variable is not felt all at once at a single point in time, but The impact is distributed over a number of future points in time. In other words, current control variables are determined by a function that can be reduced to a distributed lag function of past observations. It is possible to explain the relationship between variables in different points of time and to estimate the long-run impacts of a change in a variable on another if time lag series of explanatory variables are incorporated in the model specification. In this study, distributed lag structure is applied to the domestic stock market model to capture the dynamic response of the market by exogenous shocks. The Domestic market is found more responsive to the changes in foreign market factors both in the short and the long run.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.235-245
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• 2012

Logistic discrimination is an useful statistical technique for quantitative analysis of financial service industry. Especially it is not only easy to be implemented, but also has good classification rate. Generalized additive model is useful for credit scoring since it has the same advantages of logistic discrimination as well as accounting ability for the nonlinear effects of the explanatory variables. It may, however, need too many additive terms in the model when the number of explanatory variables is very large and there may exist dependencies among the variables. Mixtures of factor analyzers can be used for dimension reduction of high-dimensional feature. This study proposes to use the low-dimensional factor scores of mixtures of factor analyzers as the new features in the generalized additive model. Its application is demonstrated in the classification of some real credit scoring data. The comparison of correct classification rates of competing techniques shows the superiority of the generalized additive model using factor scores.

• Korean Journal of Exercise Nutrition
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• v.24 no.1
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• pp.9-13
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• 2020

[Purpose] This preliminary study aimed to develop a regression model to estimate the resting metabolic rate (RMR) of young and middle-aged Koreans using various easy-to-measure dependent variables. [Methods] The RMR and the dependent variables for its estimation (e.g. age, height, body mass index, fat-free mass; FFM, fat mass, % body fat, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, and resting heart rate) were measured in 53 young (male n = 18, female n = 16) and middle-aged (male n = 5, female n = 14) healthy adults. Statistical analysis was performed to develop an RMR estimation regression model using the stepwise regression method. [Results] We confirmed that FFM and age were important variables in both the regression models based on the regression coefficients. Mean explanatory power of RMR₁ regression models estimated only by FFM was 66.7% (R²) and 66.0% (adjusted R²), while mean standard errors of estimates (SEE) was 219.85 kcal/day. Additionally, mean explanatory power of RMR₂ regression models developed by FFM and age were 70.0% (R²) and 68.8% (adjusted R²), while the mean SEE was 210.64 kcal/day. There was no significant difference between the measured RMR by the canopy method using a metabolic gas analyzer and the predicted RMR by RMR₁ and RMR₂ equations. [Conclusion] This preliminary study developed a regression model to estimate the RMR of young and middle-age healthy Koreans. The regression model was as follows: RMR₁ = 24.383 × FFM + 634.310, RMR₂ = 23.691 × FFM - 5.745 × age + 852.341.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.161-165
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• 2022

The purpose of this study was to improve children's positive perceptions of the future by examining the relationship between parental attachment, parental involvement in learning, and children's perceptions of the future, and by identifying the specific influence of each variable on children's perceptions of the future. Frequency analysis and descriptive statistical analysis were performed on data from the 12th year of the Korean Children's Panel (2019), and Pearson's moment correlation coefficient was calculated for correlation analysis between variables. Multiple regression analysis was performed to examine the explanatory power of parental attachment to children's perception of the future and parental participation in learning. The research results are as follows. First, the correlations among all the latent variables of parental attachment, parental participation in learning, and children's future perception showed significant correlations. Second, the explanatory power of children's perception of the future was found in the order of 'mother' trust, 'family'-based participation, 'father' trust, and 'mother' communication. These results suggested that parental trust and warm, warm participation in home-based learning were important variables in children's positive perception of the future.

• Journal of the Korean Geosynthetics Society
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• v.21 no.2
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• pp.21-30
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• 2022

Shear strength is the most important indicator in the evaluation of rock slope stability. It is generally estimated by comparing the results of existing literature data, back analysis, experiments and etc. There are additional variables related to the state of discontinuity to consider in the shear strength of the rock slope. It is difficult to determine whether these variables exist through drilling, and it is also difficult to find an exact relationship with shear strength. In this study, the data calculated through back analysis were used. The relationship between previously considered variables was applied to deep learning and the possibility for estimating shear strength of rock slope was explored. For comparison, an existing simple linear regression model and a deep learning algorithm, a deep neural network(DNN) model, were used. Although each analysis model derived similar prediction results, the explanatory power of DNN was improved with a small differences.