• Horticultural Science & Technology
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• v.32 no.2
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• pp.241-251
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• 2014

This study was carried out to develop a simple, rapid and reliable assessment model to predict cold tolerance in Pittosporum tobira, a broad-leaved evergreen commonly used in the southern region of South Korea, which can minimize the possible experimental errors appeared in a electrolyte leakage test for cold tolerance assessment. The modeling procedure comprised of regrowth test and a electrolyte leakage test on the plants exposed to low temperature treatments. The lethal temperatures estimated from the methodological combinations of a electrolyte leakage test including tissue sampling, temperature treatment for potential electrical conductivity, and statistical analysis were compared to the results of the regrowth test. The highest temperature showing the survival rate lower than 50% obtained from the regrowth test was $-10^{\circ}C$ and the lethal was $-10^{\circ}C{\sim}-5^{\circ}C$. Based on the results of the regrowth test, several methodological combinations of electrolyte leakage tests were evaluated and the electrolyte leakage lethal temperatures estimated using leaf sample tissue and freeze-killing method were closest to the regrowth lethal temperature. Evaluating statistical analysis models, linear interpolation had a higher tendency to overestimate the cold tolerance than non-linear regression. Consequently, the optimal model for cold tolerance assessment of P. tobira is composed of evaluating electrolyte leakage from leaf sample tissue applying freeze-killing method for potential electrical conductivity and predicting lethal temperature through non-linear regression analysis.

• Journal of Biosystems Engineering
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• v.40 no.3
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• pp.296-304
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• 2015

Purpose: The balance between miscanthus production and its cost effectiveness depends greatly on its moisture content during post processing. The objective of this research was to measure the moisture content using a non-destructive and non-contact methodology for in situ applications. Methods: The moisture content of comminuted miscanthus was controlled using a closed chamber, a humidifier, a precision weigher, and a real-time monitoring software developed in this research. A CMOS sensor equipped with $50{\times}$ magnifier lens was used to capture magnified images of the conditioned materials with moisture content level from 5 to 30%. The hypothesis is that when light is incident on the comminuted particles in an inclined manner, higher moisture content results in light being reflected with a higher intensity. Results: A linear regression analysis for an initiative hypothesis based on general histogram analysis yielded insufficient correlations with low significance level (<0.31) for the determination coefficient. A significant relationship (94% confidence level) was determined at level 108 in a reverse accumulative histogram proposed based on a revised hypothesis. A linear regression model with the value at level 108 in the reverse accumulative histogram for a magnified image as the independent variable and the moisture content of comminuted miscanthus as the dependent variable was proposed as the estimation model. The calibrated linear regression model with a slope of 92.054 and an offset of 32.752 yielded 0.94 for the determination coefficient (RMSE = 0.2%). The validation test showed a significant relationship at the 74% confidence level with RMSE 6.4% (n = 36). Conclusions: To compensate the inconsistent significance between calibration and validation, an estimation model robust against various systematic interferences is necessary. The economic efficiency of miscanthus, which is a promising energy resource, can be improved by the real-time measurement of its crucial material properties.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.403-410
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• 2014

Binary logistic regression analysis is a statistical technique that explains binary response variable by quantitative or qualitative explanatory variables. In the binary logistic regression model, the probability that the response variable equals, say 1, one of the binary values is to be explained as a transformation of linear combination of explanatory variables. This is one of big barriers that non-statisticians have to overcome in order to understand the model. In this study, an educational tool is developed that explains the need of the binary logistic regression analysis using Excel VBA. More precisely, this tool explains the problems related to modeling the probability of the response variable equal to 1 as a linear combination of explanatory variables and then shows how these problems can be solved through some transformations of the linear combination.

• The Journal of the Convergence on Culture Technology
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• v.10 no.6
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• pp.735-740
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• 2024

ESG has become essential considerations for sustainable management. Domestic corporations are preparing response systems for ESG evaluations. Currently, ESG is expressed in ratings, which limits the detailed analysis of its calculations. So, it is necessary to predict and analyze ESG scores. Governance varies with each country's industrial structure, so it is important to consider its specific characteristics. Therefore, we propose a machine learning-based linear regression model to predict ESG-Governance scores for domestic companies. We collect ESG-Governance scores, financial and non-financial variables related to ESG, and preprocess the data. We apply preprocessed data to our machine learning-based linear regression model and validate model performance with 10-fold cross-validation. Our model showed 79.27% accuracy on test sets. We expect that predicting ESG-Governance score based on our model will enhance the ESG evaluation response system for domestic corporate.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.1017-1023
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• 2021

This research aims to determine the effect of customers' threat emotion and price on the decision to purchase a certain smartphone product. This study uses a quantitative method with a type of descriptive and causal research. It employs non-probability sampling with purposive sampling, with 385 respondents to answer the questionnaires. Data analysis techniques used descriptive analysis and multiple linear regression analysis. Based on the results of descriptive analysis of emotion, price and purchasing decisions are in sync with each other. The results of multiple linear regression analysis techniques indicate the threat emotion and brand trust are influential against the positive decision to purchase smartphone products. The magnitude of the influence of emotions and price have simultaneous effect on purchasing decisions and other decision variables, which are not included in this study, also play minor role in determining purchase intention, such as product quality, brand image and others. Partially, threat emotion and brand trust have a positive effect toward purchasing decisions. The magnitude of the highest influence was the one of price, then followed by emotional threats. The findings of this study suggest that psychological and behavioral effects also play important roles in determining customers' purchase decision.

• Journal of the Korean Society for information Management
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• v.29 no.2
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• pp.193-204
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• 2012

The characteristics of citation and centrality measures in citation networks can be identified using multiple linear regression analyses. In this study, we examine the relationships between bibliometric indices and centrality measures in an article-level co-citation network to determine whether the linear model is the best fitting model and to suggest the necessity of data transformation in the analysis. 703 highly cited articles in Physics published in 2004 were sampled, and four indicators were developed as variables in this study: citation counts, degree centrality, closeness centrality, and betweenness centrality in the co-citation network. As a result, the relationship pattern between citation counts and degree centrality in a co-citation network fits a non-linear rather than linear model. Also, the relationship between degree and closeness centrality measures, or that between degree and betweenness centrality measures, can be better explained by non-linear models than by a linear model. It may be controversial, however, to choose non-linear models as the best-fitting for the relationship between closeness and betweenness centrality measures, as this result implies that data transformation may be a necessary step for inferential statistics.

• Journal of Soil and Groundwater Environment
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• v.14 no.5
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• pp.41-50
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• 2009

Water levels in groundwater monitoring wells of Jeju Island were analyzed using parametric and non-parametric trend analyses. Number of used monitoring wells in the analysis are 94 among totally 106 monitoring wells and the monitoring period is greater than single year, from 2001 to 2009. For the trend analysis, both parametric (linear regression) and nonparametric (Mann-Kendall trend test and Sen's trend test) methods were adopted. Results of the linear regression analysis on daily basis indicated that about 58.5% of the monitoring wells showed a decreasing trend, and analysis using monthly median indicated that about 79.8% showed a decreasing trend. The Mann-Kendall trend test and Sen's trend test with monthly median values in confidence levels of 95% and 99% showed the same analysis results. In confidence level of 95%, 32% were decreased, 3% were increased and the remains showed no trend. However, in confidence level of 99%, 16% were decreased, 2% were increased and the remains showed no trend. The largest decline rates of water levels were detected mainly at the coast of the northwestern and southwestern parts, which is expected to closely related to the increased pumping in the urban area and tourist resort.

• The Journal of Engineering Geology
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• v.20 no.1
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• pp.35-45
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• 2010

The soil tests have been performed on the specimens obtained from about 1,150 sites including landslides and non-landslides areas in natural terrains for last 10 years. Based on the results of those tests, the average soil properties are estimated and the simple equations for estimating permeability are proposed according to geologic conditions. The average permeability in Granite and Mudstone sites is higher than other sites and the content of silt and clay in Mudstone and Gneiss sites is higher than other sites. The correlation analysis and the regression analysis were performed to estimate the coefficient of permeability according to geological conditions. As the result of the correlation analysis, the coefficient of permeability is selected as a dependent variable, and the silt and clay contents, the water contents and the dry unit weights are selected as independent variables. As the result of the regression analysis, the silt and clay contents and the void ratio were involved commonly in the linear regression equations according to geological conditions. To verify the proposed the linear regression equations, the measured result of the coefficient of permeability at other sites was compared with the result predicted with the proposed equations. As the result of comparison, there were a little bit different between them for some data. However the difference was relatively small. Therefore, the linear regression equations for estimating the coefficient of permeability according to geological conditions may be applied to Korean soils. However, these equations should be verified and corrected continuously to improve the accuracy.

• Korean Journal of Clinical Laboratory Science
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• v.45 no.2
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• pp.54-59
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• 2013

NonHDL cholesterol values have been suggested as a risk marker for cardiovascular disease. NonHDL cholesterol values were calculated, using a very simple measurement [nonHDL cholesterol=serum total cholesterol-HDL cholesterol]. This formula is very useful as a screening tool for identifying dyslipoproteinemias, risk assessment, and assessing the results of hypolipidemic therapy. The data from the 2009 Korean National Health and Nutrition Examination Survey were used. Analysis was done for 1,992 subjects with lipid panels (Cholesterol, HDL, LDLdirect and Triglycerides) results. We studied the relationship between nonHDL cholesterol and LDL cholesterol. As a result, nonHDL cholesterol values were plotted against the LDL direct and calculated values. The linear regression equation for nonHDL cholesterol and direct LDL cholesterol was $nonHDLchol=23.60+1.03{\times}LDLdirect$ (p<0.0001, $r^2=0.80$) in all subjects. The subjects were classified into triglyceride values. When triglycerides are below 400 mg/dL, the linear fit to LDL direct is found to be $[nonHDLchol=17.34+1.07{\times}LDLdirect]$ (p<0.0001, $r^2=0.88$) and to the Friedewald LDL calculation is $[nonHDLchol=23.10+1.02{\times}LDLcalc]$ (p<0.0001, $r^2=0.82$). For triglycerides above 400 mg/dL, the linear fit equation is $[nonHDLchol=87.57+0.92{\times}LDLdirect]$ (p<0.0001, $r^2=0.50$) and to the LDL calculated, it is $[nonHDLchol=142.70+0.50{\times}LDLcalc]$ (p<0.0001, $r^2=0.32$). This study provides examples of the utility of nonHDL cholesterol concentrations in clinical medicine.

• Journal of Preventive Medicine and Public Health
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• v.36 no.2
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• pp.125-130
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• 2003

Objectives : This study evaluated the stress of community residents using the General Health Questionnaire, GHQ-60, as an instrument of stress measurement. Methods : The study included 2100 residents, aged 20 and over, living in three areas, a large city, a medium sized city and a rural area, between June and September 2001. A questionnaire interviewing method was used to collect data. The data were analyzed using a t-test, ANOVA, Pearson's correlation coefficients and multiple regression analysis. Results : In this study, the degree of stress, as measured by the GHQ-60, was shown to be significantly higher in the following categories: females, people over 60 years old, people engaged in the primary industries and labor work, low incomes, the divorced and the bereaved, people who received no more than an elementary education, people who suffer from chronic diseases and non-exercisers. A factor analysis suggested that there were three factors of social dysfunction factors; psychosomatic symptom, and depression and anxiety, The social dysfunction factors was statistically significant for the groups described above. The factor of psychosomatic symptoms was statistically significant in the rural residents, and in the groups describedabove. The depression and anxiety factor was statistically significant in the large city residents, people aged between 20-29 years, students, unmarried persons, university graduates and those having suffered from chronic diseases. From the multiple linear regression analyses, chronic disease, exercise, gender and income, proved to be significant stress related factors Conclusions : This study suggests that special attention should be given to the management of the chronic invalided, non-exercisers, females and snail income earners, in order to maintain and promote the psychological health of residents in a community.