• Journal of Family Resource Management and Policy Review
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• v.6 no.1
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• pp.103-115
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• 2002

The purpose of this study was to analyze which factor, influenced the business and family performance success. Data were obtained from 248 family households. Proxy variable of the business performance was gross business income and of the family performance was the Family AFGAR scores. The multiple regression analysis was conducted for both the business performance equation and family performance equation. The main results of this study were as followings: The results indicated the effects of various business and family characteristics on performance and their contributions to the business and family performance model. Nine explanatory variables such as sex, being home-based, number of hours worked per week, number of family employee, number of nonfamily employee, total asset, the presence of young child under 6, nonbusiness income, and role conflicts were statistically significant in the business performance equation and three explanatory variables such as the hours worked per week, family stress scores, and role conflicts were statistically significant in the family performance equation. The results indicated the need for a more comprehensive view of family business performance.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05b
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• pp.189-192
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• 2006

This study was performed to suggest the mediocre prediction equation of chloride diffusion coefficient which is used to estimate the service life of marine concrete, in order to provide the useful data for concrete mix design of marine concrete. As a result, the mediocre prediction equation of chloride diffusion coefficient which set W/B and mineral admixture replacement ratio as parameters was presented by performing the multivariate non linear regression analysis.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.129-141
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• 2003

The precise prediction of ground displacement plays an important role in planning and constructing tunnels. In this study, an equation for predicting the surface and crown settlement is suggested by examining the theories of ground movement caused by tunnel excavation. From the 3D numerical modeling, the reinforcement effect of UAM (Umbrella Arch Method) is quantitatively analyzed with respect to deformation modulus and overburden. By using a regression technique for the numerical results, an equation for predicting the settlement is suggested.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.10a
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• pp.227-234
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• 2003

This study presents the stress evaluation equations of circular column-box beam connection in steel frame piers. FEM analysis were carried out for circular column-box beam connection. Analysis models were made for design parameters such as joint angle, span length-width ratio(L/B), sectional-area ratio(S=A/sub w/A/sub f/), and circular column-box beam stiffness ratio(Ic/Ib). Analysis results were compared to the existing equation. Based on analysis results the stress evaluation equations of circular column-box beam connection are proposed by regression analysis.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.69.1-69.1
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• 2020

While Dark Energy is one of the explanations for the accelerating expansion of the Universe, its nature remains a mystery. The standard (flat) ΛCDM model is consistent with cosmological observations: type Ia Supernova, BAO, CMB, and so on. However, the analysis of observations assuming a model, model-dependent approach, is likely to bias the results towards the assumed model. In this poster, I will introduce model-independent approach with Gaussian process and the application of Gaussian process regression to reconstruct the equation of state of dark energy.

• Journal of The Korean Association For Science Education
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• v.22 no.5
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• pp.1010-1029
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• 2002

The purpose of this research was to survey middle school students' preference for science and its causal factors, so as to analyze the causal relationships between them. Preference for science and its causal factors were defined theoretically, and a theoretical model was constructed to measure them and analyze the causal relationship by structural equation modeling. According to the theoretical model and a pilot test, a questionnaire was developed with three parts; the background information of a respondent, the preference for science, and the causal factors of preference. The questionnaire was administered to one class per grade of randomly selected 8 middle schools from 4 areas across the country, and 819 students' data were collected. Preference for science was defined as a state of mind. It revealed to what extent, and how, one likes science. It consisted of 3 categories - 'emotional response', 'behavioral volition', 'valuational comprehension', and each category was divided into two subcategories. Causal factors affecting the preference for science consisted of three categories - personal, educational and social factors, and each was divided into 2 or 3 subcategories. Middle school students' preference for science was middling as a total. Curiosity about contents of science and valuation of science were high, comparatively, but behavioral volition about science was especially low. Students' responses to the causal factors were relatively high in every educational factor and sociocultural valuation of social factors, but relatively low in socioeconomic rewards of social factors, and especially low in personal factors. The causal relationship about the preference for science was investigated by multiple regression analysis and path analysis, using the structural equation model. Multiple regression analysis about the preference for science and its causal factors revealed important factors. The important factors were personal ability, the personal traits, rewards in school science, and contents of school science in order of magnitude of standardized regression coefficient ${\beta}$. Stepwise regression analysis with each of the subcategories of the preference for science as dependent variables showed what factors were important in each subcategory. According to the result of structural equation modeling, personal factors affected 'emotional response' and 'behavioral volition' directly, and social factors affected 'valuational comprehension' directly. Educational factors affected all categories of the preference for science by influencing not only 'emotional response' and 'valuational comprehension' directly, but also 'behavioral volition' indirectly. The way to promote middle school students' preference for science was suggested, based on the analysis result.

• Tunnel and Underground Space
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• v.22 no.4
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• pp.276-283
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• 2012

Changes in rock properties due to freezing and thawing cycles ranging from $-20^{\circ}C$ to $10^{\circ}C$ were checked for the typical Korean rocks: granite (weathered), limestone, sandstone, tuff, shale and basalt. The porosity, seismic velocity, shore hardness and specific gravity were measured every 10 cycles for each type of rock up to 40 cycles. The specific gravity was rarely changed. Granite (w), shale and basalt decreased gradually in their shore hardness and seismic velocity values, these values for limestone, sandstone and tuff changed only a very little. The porosity increased in the granite (w), shale and basalt, whereas in the others it did not change. Due to the low tensile strength with high porosity, granite (w), shale and basalt were susceptible to the F-T cycles. A linear regression equation was calculated based on the experiment results according to properties and types of rock. The relationship between the freeze-thaw sensitivity (=initial porosity/initial tensile strength) and the coefficients of the regression equation was examined. With additional experimental data, the coefficients of the regression equation can be estimated using the F-T sensitivity. This makes it possible to predict the properties of rock as affected by freeze-thaw weathering by only measuring the initial properties without knowledge of the regression equation coefficients for each type of rock.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.40
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• pp.1-14
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• 1996

We develop a dynamic demand forecasting model compared to regression analysis model and AutoRegressive Integrated Moving Average(ARIMA) model. The dynamic model can apply to the current dynamic data to forecasts through introducing state equation. A multiple regression model and ARIMA model using given data are designed via the model analysis. The forecasting fitness evaluation between the designed models and the dynamic model is compared with the criterion of sum of squared error.

• Journal of the Korean Statistical Society
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• v.35 no.2
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• pp.213-224
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• 2006

We investigate the influence of subjects or observations on regression coefficients of generalized estimating equations using the influence function and the derivative influence measures. The influence function for regression coefficients is derived and its sample versions are used for influence analysis. The derivative influence measures under certain perturbation schemes are derived. It can be seen that the influence function method and the derivative influence measures yield the same influence information. An illustrative example in longitudinal data analysis is given and we compare the results provided by the influence function method and the derivative influence measures.

• Journal of the military operations research society of Korea
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• v.5 no.2
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• pp.27-38
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• 1979

This method explores a method of aggregating the measures of effectiveness of a weapon system from its characteristics. With this method, the constant sum method and multiple regression are used to develop a functional relationship between system effectiveness and system characteristics. As an example, a study of a tank weapon system was${\cdot}$conducted with data from the U.S. Army Armor School. It was concluded that the aggregation method is feasible, and that for the tank system studied, the reciprocals of system characteristics give a good estimating equation for measuring tank system effectiveness.