• Journal of Environmental Science International
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• v.22 no.8
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• pp.953-963
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.173-180
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• 2009

It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.

• The Korean Journal of Community Living Science
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• v.25 no.1
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• pp.75-87
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• 2014

This study examines the relationships between elementary school students' multiple intelligences, prosocial behaviors, and school adjustments. The sample included 367 students from two elementary schools, and data were collected using the multiple-intelligence index, the prosocial behavior inventory, and the school adjustment index. A statistical analysis was conducted using t-test, a one-way ANOVA (Duncan's test), and a multiple regression analysis. According to the results, there were significant differences in the students' prosocial behaviors according to their multiple intelligences in terms of their sharing, kindness, cooperation, help, sympathy, and protection. In addition, there were significant differences in their school adjustments according to their multiple intelligence in terms of their adjustments to their teachers, friends, studies, and rules and events. The students' multiple intelligence was a significant predictors of their prosocial behaviors and school adjustments. In particular, their interpersonal intelligences, intrapersonal intelligences, and linguistic intelligences were significant predictors of their prosocial behaviors and school adjustments. These results suggest that elementary school students should be encouraged to develop their multiple intelligence to facilitate their prosocial behaviors and school adjustments.

• Korean Journal of Adult Nursing
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• v.21 no.4
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• pp.391-402
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• 2009

Purpose: This study was conducted to identify predictors of the stage of change for smoking cessation of male university students on the basis of the Transtheoretical model (TTM). Methods: The 388 current smokers or exsmokers who agreed to participate were recruited from three areas in Korea from August 2 to September 5, 2006. Data was analyzed using a SPSS program for descriptive statistics, ANOVA, and multiple logistic regression. Results: Most subjects (76.8%) were current smokers. According to stages of change, there were statistically significant differences in self efficacy, smoking temptation, decisional balance (cons, pros), and processes of change. The predictors of transition from contemplation to preparation were behavioral and experiential processes. The predictors of transition from preparation to action were pros of smoking and experiential process. The predictors of transition from action to maintenance were cons of smoking and behavioral process. Conclusion: Specific nursing interventions based on stages of change need to be developed for smoking cessation of male university students.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.803-804
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• 2006

This paper presents a multiple branch predictor with perceptrons. We describe our design and evaluate it with the SPEC 2000 benchmarks. Our predictor achieves increased accuracy than the previous multiple branch predictors.

• Journal of Industrial Technology
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• v.28 no.B
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• pp.101-109
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• 2008

In general, non-stationary or chaos time series forecasting is very difficult since there exists a drift and/or nonlinearities in them. To overcome this situation, we suggest a new prediction method based on multiple model TS fuzzy predictors combined with preprocessing of time series data, where, instead of time series data, the differences of them are applied to predictors as input. In preprocessing procedure, the candidates of optimal difference interval are determined by using con-elation analysis and corresponding difference data are generated. And then, for each of them, TS fuzzy predictor is constructed by using k-means clustering algorithm and least squares method. Finally, the best predictor which minimizes the performance index is selected and it works on hereafter for prediction. Computer simulation is performed to show the effectiveness and usefulness of our method.

• Child Health Nursing Research
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• v.17 no.3
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• pp.181-189
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• 2011

Purpose: This study was done to investigate the predictors of maternal confidence between primipara and multipara mothers. Methods: The participants enrolled in this study were 145 mothers between 2 and 5 days postpartum. Data were collected using a questionnaire and analyzed using descriptive analysis, t-test, $X^2$ test, ANOVA, Pearson correlation coefficients, and stepwise multiple regression. Results: The predictors of maternal confidence among primipara mothers were social support, self-esteem, mode of delivery ($R^2$=.287, p<.001). Whereas, gender of baby, antenatal fetal attachment, and educational status explained maternal confidence among multipara mothers ($R^2$=.270, p<.001). Conclusion: The results indicate that when nurses develop programs to encourage maternal confidence for new mothers, they should take into account the differences between predictors of maternal confidence according to the mothers' experience of birth.

• Journal of Korean Academy of Nursing
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• v.31 no.4
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• pp.571-583
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• 2001

Purpose: The purpose of this study was to examine the levels of burnout experienced by Korean hospital nurses (N=198), and to identify predictors of their nursing outcomes such as job performance, work satisfaction, and productivity. Method: Hierarchical multiple regression was used to identify predictors of each nursing outcomes. Included predictors were nurses' general characteristics, work- related characteristics (role stress and perceived control), and burnout. Results: Korean nurses experienced higher levels of burnout compared to the cutoffs suggested by Maslach and Jackson (1986) and to those in the USA. For each nursing outcome, predictor variables explained 39% of the variance in role performance, 30% of the variance in work satisfaction, and 38% of variance in productivity. Higher personal accomplishment, lower role ambiguity, being staff nurses, and lower emotional exhaustion were related to higher job performance, and higher productivity. Lower role conflict and role ambiguity were also related to higher work satisfaction. Conclusion: Based on the findings of this study, managemental interventions for nurses to reduce their burnout experience are needed. Further study in this area is warranted.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.19 no.4
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• pp.474-482
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• 2012

Purpose: The purposes of this study were to identify knowledge, health belief and compliance in patients with hypertension and to identify the most important predictors for compliance of hypertensive patient. Method: The participants in this study were 117 patients who were receiving treatment for hypertension at E. university hospital or one of three local clinics in D-city. Data were collected using a knowledge measurement instrument, health belief scale, and an instrument on compliance. Collected data were analyzed using $X^2$ test, ANOVA, multiple linear regression with PASW statistics 18.0 program. Results: There were statistically significantly positive correlations between knowledge of hypertension and health belief, health belief and compliance. But there was no correlation between knowledge of hypertension and compliance. In the multiple regression analysis, perceived barriers, perceived severity, perceived benefits were significant predictors to explain compliance and accounted for 54.1% of the variance in compliance. Conclusion: The results of the study indicate that health belief and compliance are significantly strongly correlated. Thus it is suggested that nursing interventions to improve compliance should include nursing care plans to increase health belief, perceived severity, perceived benefit and to decrease perceived barrier.

• Journal of Korean Academy of Nursing
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• v.44 no.5
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• pp.515-524
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• 2014

Purpose: The purpose of this study was to identify whether the couple perceived breast cancer as a traumatic event, to evaluate the association among posttraumatic growth, dyadic adjustment, and quality of life and to explore the predictors affecting quality of life of the couple. Methods: A cross-sectional comparative survey design was utilized. Participants were 57 couples recruited from a national cancer center in Korea. Data were analyzed using paired t-test, McNemar test and independent t-test. On the basis of variables found to be significantly associated with quality of life, multiple regression was used to examine the simultaneous influence of multiple predictors. Results: Breast cancers survivors and spouses perceived breast cancer as a traumatic event (43.9% and 24.6%, respectively). The global quality of life was explained by perception as trauma (${\beta}$= -19.79) and posttraumatic growth (${\beta}$=0.46) in survivors, and perception as trauma (${\beta}$= -18.81) and dyadic adjustment (${\beta}$=0.53) in spouses. Conclusion: Results suggest that future research should use qualitative methods to evaluate why contemplating reasons for cancer contributed to posttraumatic growth, examine other potential predictors of quality of life such as dyadic adjustment and intimacy, and identify links between posttraumatic growth and other psychological outcomes such as distress and well-being, using prospective analyses.