• Korean Journal of Remote Sensing
• /
• v.33 no.1
• /
• pp.25-35
• /
• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• The Journal of Korean Society for School & Community Health Education
• /
• v.10 no.2
• /
• pp.15-27
• /
• 2009

Background and Goals: This study set out to apply the Theory of Planned Behavior (TPB), which is known to provide good explanations about human behavior, and test it to see if it could predict safety behavior by affecting the intention for safety behavior and perceived behavioral control and if intention for safety behavior would be influenced by attitude toward behavior, subjective norm, and perceived behavioral control. Methods: The subjects were 98 dental technology majors in D City. The questionnaires were distributed, filled out and collected on the spot. Each item was measured on a seven-point scale, and it's interpreted that the higher mean of each item would translate into safety behavior. Results: The analysis results of the Theory of Reasoned Action (TRA) variables indicate that only subjective norm ($\beta$ = .528, p < .000) had explanatory power of 27.2% (F = 37.170, P <.001) for intention for safety behavior. The results show that subjective norm and attitude toward behavior affect intention for safety behavior. The analysis results of the TPB variables revealed that intention for safety behavior had explanatory power of 26.6% (F = 36.072, p <.000) for behavior. When intention was added by perceived behavioral control, the explanatory power increased to 34.5% (F = 26.530, p <.000). And when it's added by knowledge, the explanatory power increased to 39.0% (F =21.661, p <.000). The results suggest that intention has the biggest influence on predicting safety behavior. Conclusion: The results show that the TPB model by Ajzen (1985) has greater forecasting power for intention and act of safety behavior than the TRA model by Fishbein & Ajzen (1980) and the TPB model can applied in the prediction of safety behavior. Thus safety behavior is considered as behavior whose determination control is limited. And safety education programs that add knowledge to the TPB variables will help the students promote their safety behavior.

• The Korean Journal of Applied Statistics
• /
• v.20 no.1
• /
• pp.195-204
• /
• 2007

In order to represent multiple regression data, an alternative graphical method, called as SSR Plot, is proposed by using geometrical description methods. This plot uses the relation that the sum of sqaures for regression (SSR) of two explanatory variables is known as the sum of the SSR of one variable and the increase in the SSR due to the addition of other variable to the model that already contains a variable. This half circle shaped SSR plot contains vectors corresponding explanatory variables. We might conclude that some explanatory variables corresponding to vectors which locate near the horisontal axis do affect the response variable. Also, for the regression model with two explanatory variables, a magnitude of the angle between two vectors can be identified for suppression.

• The Korean Journal of Applied Statistics
• /
• v.26 no.3
• /
• pp.441-452
• /
• 2013

This study analyzed the relationship between key film and a box office record success factors based on movies released in the first quarter of 2013 in Korea. An over-fitting problem can happen if there are too many explanatory variables inserted to regression model; in addition, there is a risk that the estimator is instable when there is multi-collinearity among the explanatory variables. For this reason, optimal variable selection based on high explanatory variables in box-office performance is of importance. Among the numerous ways to select variables, LASSO estimation applied by a generalized linear model has the smallest prediction error that can efficiently and quickly find variables with the highest explanatory power to box-office performance in order.

• Journal of Korea Technology Innovation Society
• /
• v.10 no.2
• /
• pp.183-205
• /
• 2007

An investigation was undertaken of the optimal discriminant model for predicting the likelihood of insolvency in advance for medium-sized firms based on the technology evaluation. The explanatory variables included in the discriminant model were selected by both factor analysis and discriminant analysis using stepwise selection method. Five explanatory variables were selected in factor analysis in terms of explanatory ratio and communality. Six explanatory variables were selected in stepwise discriminant analysis. The effectiveness of linear discriminant model and logistic discriminant model were assessed by the criteria of the critical probability and correct classification rate. Result showed that both model had similar correct classification rate and the linear discriminant model was preferred to the logistic discriminant model in terms of criteria of the critical probability In case of the linear discriminant model with critical probability of 0.5, the total-group correct classification rate was 70.4% and correct classification rates of insolvent and solvent groups were 73.4% and 69.5% respectively. Correct classification rate is an estimate of the probability that the estimated discriminant function will correctly classify the present sample. However, the actual correct classification rate is an estimate of the probability that the estimated discriminant function will correctly classify a future observation. Unfortunately, the correct classification rate underestimates the actual correct classification rate because the data set used to estimate the discriminant function is also used to evaluate them. The cross-validation method were used to estimate the bias of the correct classification rate. According to the results the estimated bias were 2.9% and the predicted actual correct classification rate was 67.5%. And a threshold value is set to establish an in-doubt category. Results of linear discriminant model can be applied for the technology financing banks to evaluate the possibility of insolvency and give the ranking of the firms applied.

• Journal of The Korean Association For Science Education
• /
• v.33 no.2
• /
• pp.522-537
• /
• 2013

This study analyzed the relationship among the construct level of analogical reasoning, prediction and uncertainty, and the construction of an explanatory model that were produced during small group discussions for scientific problem solving. This study was participated in by 8 students of K University divided into 2 teams conducting scientific problem solving. The participants took part in discussions in groups after achieving scientific problem solving individually. Through individual interviews afterwards, changes in their thinking through discussion activities were looked into. The results are as follows: The analogy at the Entities/Attributes level was used to make people clearly understand the characteristics of certain objects or entities in the discussions. The analogy at the Configuration/Motion level that was produced during the discussions ensured other participants to predict the results of problem solving. The analogy at the Mechanism/Causation level changed the structure of problem situations either to help other participants to reconstruct the explanatory model or to come up with a new situation that was never been through before to justify the created mechanism and through this, the case of creating Thought Experiments during the discussions were observed. if looking into the changes of analogies, each individual's analogic paradigm during the discussions were shown as production paradigm, reception-production paradigm, production-reception paradigm, and reception paradigm. The construction and reconstruction of the explanatory model were shown in analogic production paradigm, and in the reception paradigm of an analogy, participants changed their predictions or their certainty.

• Journal of Korean Institute of Industrial Engineers
• /
• v.40 no.3
• /
• pp.283-290
• /
• 2014

In the flat panel display industry, to meet production target quantities and the deadline of production, the scheduler and dispatching systems are major production management systems which control the order of facility production and the distribution of WIP (Work In Process). Especially the delivery time is a key factor of the dispatching system for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors of the delivery time and to build the delivery time forecasting model. To select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the accelerated failure time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the mean square error (MSE) criteria, the AFT model decreased by 33.8% compared to the statistics prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing the delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.

• Journal of the Korean Geographical Society
• /
• v.51 no.2
• /
• pp.219-233
• /
• 2016

In social science fields, statistical models are used almost exclusively for causal explanation, and explanatory modeling has been a mainstream until now. In contrast, predictive modeling has been rare in the fields. Hence, we focus on constructing the predictive non-parametric model, instead of the explanatory model. Gangnam-gu, Seoul was chosen as a study area and we collected single-family house sales data sold between 2011 and 2014. We applied non-parametric models proposed in machine learning area including generalized additive model(GAM), random forest, multivariate adaptive regression splines(MARS) and support vector machines(SVM). Models developed recently such as MARS and SVM were found to be superior in predictive power for house price estimation. Finally, spatial autocorrelation was accounted for in the non-parametric models additionally, and the result showed that their predictive power was enhanced further. We hope that this study will prompt methodology for property price estimation to be extended from traditional parametric models into non-parametric ones.

• Communications for Statistical Applications and Methods
• /
• v.20 no.2
• /
• pp.129-136
• /
• 2013

Linear regression is the most basic statistical model for exploring the relationship between a numerical response variable and several explanatory variables. Logistic regression secures the role of linear regression for the dichotomous response variable. In this paper, we propose a biplot-type display of the multivariate data guided by the linear regression and/or the logistic regression. The figures show the directional flow of the response variable as well as the interrelationship of explanatory variables.

• Journal of the Korean Home Economics Association
• /
• v.38 no.1
• /
• pp.39-49
• /
• 2000

This research investigated the effect of socio-economic variables and economic distress variables on the university students' coping behavior in meal management. The data used in this research included 544 university students in Ulsan Areas. The independent explanatory power of socio-economic variables was larger than economic distress variables. But the explanatory power was increased in the regression analysis model that was included both the socio-economic variables and the economic distress variables. The influencing variables that effected the level of coping behavior in meal management were the amount of discretionary expenditure, gender, status of housing, employment distress and income distress.