• Korean Journal of Occupational Health Nursing
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• v.22 no.2
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• pp.121-129
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• 2013

Purpose: This study is designed to verify affecting variables to turnover intention of nurses in general hospital. Methods: The data were from the self-reported questionnaire responses of 168 nurses in five general hospitals 300-400 beds in Seoul and Gyungi province and analyzed by frequency and percentage, t-test, ANOVA and Sheffe's test and stepwise multiple regression. Results: The means of turnover intention were $3.14{\pm}0.87$, job overload, $3.54{\pm}0.67$, role ambiguity, $2.87{\pm}0.71$ and burnout, $2.68{\pm}0.72$. A significant correlation was found among turnover intention and job overload (r=.24, p<.001), role ambiguity (r=.30, p<.001), and burn out (r=.58, p<.001). The factors that affect turnover intention from the result of multiple regression by the stepwise selection, were emotional exhaustion, role ambiguity, and diminished personal accomplishment. Conclusion: Based on the results, to reduce turnover intention of nurses, emotional support should be provided and also range of roles be clearly defined. In addition, it is needed to improve the working conditions for nurses to get a sense of accomplishment.

• ETRI Journal
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• v.40 no.6
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• pp.802-812
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• 2018

Disease discrimination using an electronic nose is achieved by measuring the presence of a specific gas contained in the exhaled breath of patients. Many studies have reported the presence of acetone in the breath of diabetic patients. These studies suggest that acetone can be used as a biomarker of diabetes, enabling diagnoses to be made by measuring acetone levels in exhaled breath. In this study, we perform a chemical sensor array optimization to improve the performance of an electronic nose system using Wilks' lambda, sensor selection based on a principal component (B4), and a stepwise elimination (SE) technique to detect the presence of acetone gas in human breath. By applying five different temperatures to four sensors fabricated from different synthetic materials, a total of 20 sensing combinations are created, and three sensing combinations are selected for the sensor array using optimization techniques. The measurements and analyses of the exhaled breath using the electronic nose system together with the optimized sensor array show that diabetic patients and control groups can be easily differentiated. The results are confirmed using principal component analysis (PCA).

• Journal of KIISE:Software and Applications
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• v.41 no.8
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• pp.598-604
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• 2014

In this paper, we develop stepwise regression data envelopment model to select important variables. We formulate null hypothesis to understand the importance of each variable and use Kruskal-Wallis test for this purpose. If the Kruskal-Wallis test does reject the null hypothesis this will imply there is significant fluctuation in the efficiency score relative to base model. And therefore we have to further check the pair of variables that causes the fluctuation in order to determine its importance using Conover-Inman test. The proposed models helps understand the extent of misclassification decision making units as efficient/inefficient when variables are retained or discarded alongside provides useful managerial prescription to make improvement strategies.

• Journal of the Korean Society for Library and Information Science
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• v.27
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• pp.291-339
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• 1994

The purpose of this study is to find personal characteristics that affect users' cognitive characteristics of system, and to verify correlations between this users' cognitive characteristics and selection of system usage in using end -user searching systems (EUSS), For corroborative analysis of this study, preliminary model was constructed referring to Davis' Technology Acceptance Model. The model consists of exogenous variables (personal characteristics) , parameter variables (perceived usefulness, perceived ease of use), and effect variables (selection of system usage), When exogenous variables affect parameter variables, exogenous variables are independent variables and parameter variables are dependent variables. In addition, in correlation of parameter variables, which have been affected by exogenous variables, with effect variables, parameter variables are independent variables and effect variables are dependent variables, As for the research methodology, this study regards the Academic Information System connected with the Internet as EUSS, So questionnaires have been sent to researchers in universities who were conducting direct searching for the system. 229 valid responses to questionnaires have been analyzed according to Pearson Correlation Analysis and Stepwise Selection of Multiple Regression in the statistical software packages, 'SPSS PC+'. The findings and conclusions made in this study are summarized as follows; 1. Among the personal characteristics (age, disciplinary, computer literacy level, perceived usefulness of use education and training, perceived satisfaction of end-user searching, perceived satisfaction of system characteristics), all characteristics but age affect perceived usefulness and perceived ease of use. Specifically, perceived satisfaction of end user searching and perceived satisfaction of system characteristics most affect perceived usefulness and perceived ease of use respectively. 2. Perceived usefulness and perceived ease of use have a direct effect on selection of system usage in using EUSS. 3, Perceived usefulness more affect selection of system usage than perceived ease of use in using EUSS.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.5
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• pp.149-162
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• 2018

It is necessary to select the appropriate global climate model (GCM) to take into account the impacts of climate change on integrated water management. The objective of this study was to develop the selection technique of representative GCMs for uncertainty in climate change scenario. The selection technique which set priorities of GCMs consisted of two steps. First step was evaluating original GCMs by comparing with grid-based observational data for the past period. Second step was evaluating whether the statistical downscaled data reflect characteristics for the historical period. Spatial Disaggregation Quantile Delta Mapping (SDQDM), one of the statistical downscaling methods, was used for the downscaled data. The way of evaluating was using explanatory power, the stepwise ratio of the entire GCMs by Expert Team on Climate Change Detection and Indices (ETCCDI) basis. We used 26 GCMs based on CMIP5 data. The Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios were selected for this study. The period for evaluating reproducibility of historical period was 30 years from 1976 to 2005. Precipitation, maximum temperature, and minimum temperature were used as collected climate variables. As a result, we suggested representative 13 GCMs among 26 GCMs by using the selection technique developed in this research. Furthermore, this result can be utilized as a basic data for integrated water management.

• Horticultural Science & Technology
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• v.29 no.4
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• pp.345-351
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• 2011

This study was carried out to develop a model system using selection method for disease resistant plant breeding programs using a herbicide bialaphos-resistant patII gene as a gene-based marker. Spraying bialaphos could eliminate the susceptible plants from the segregating populations such as ${F_2}^{\prime}s$ and thereafter. Tomato cv. Momotaro-yoke was transformed with patII gene 60 independent transformants were acquired. Total 42 transformants were analyzed in transgene copy numbers by Southern blotting and the segregation ratios for the bialaphos resistance. Statistical analysis revealed that the transgene copy numbers and the segregation ratios were not always coincided, especially having the tendency of underestimating the real numbers of the transgenes in the multicopy lines. A two-stepwise screening method was applied to select $T_1$ tomato plants which linked the transgenic patII to a disease resistance gene (I2 and Ve). Based on the resistant to susceptible ratios, T-20 plant was finally selected due to the estimated linkage 12-13 cM between the patII gene to the I2 gene on chromosome 11. This newly developed system could be applied to any economical crop in breeding programs.

• Journal of Animal Science and Technology
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• v.52 no.2
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• pp.83-90
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• 2010

Hanwoo proven bulls have been selected since 1987 and consequently contributed to farmers for the improvement of beef cattle in Korea. The demand for the quality beef production as well as higher production efficiency was erupted after early 2000 as relatively cheap imported beef released. Therefore the pressure on the reformation of selection index for Hanwoo proven bulls have been piled up to furnish with Hanwoo's competitive. A total of 734 progeny test data were analyzed to select traits and their weights in the selection index to meet the beef market requirement. Regression analysis with stepwise selection method was used to select proper trait and its weight for selection index. A series of computer simulation was carried out to compare the currently using selection index with the alternate two selection indices proposed in this study. New selection index using standardized breeding values of Loin eye Muscle Area (LMA), Backfat Thickness (BFT) and Marbling Score (MS) with weight ratio 1:-1:6 was proposed. Results showed higher performance in improving MS and BFT gain by 22% and 31% still holding 86%~89% of genetic gain achieved by current index in Carcass Weight (CW) and LMA when new selection index was fitted. Because, new index has little consideration for production cost, further research should be performed to build selection index including cost and income simultaneously.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.775-784
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• 2022

In this study, to determine the optimal order of the full-logged I-D-F polynomial equation, which is mainly used to calculate the probable rainfall over a temporal rainfall duration, the probable rainfall was calculated and the regression coefficients of the full-logged I-D-F polynomial equation was estimated. The optimal variable of the polynomial equation for each station was selected using a stepwise selection method, and statistical significance tests were performed through ANOVA. Using these results, the statistically appropriately calculated rainfall intensity equation for each station was presented. As a result of analyzing the variable selection outputs of the full-logged I-D-F polynomial equation at 9 stations in Gyeongbuk, the 1^st to 3^rd order equations at 6 stations and the incomplete 3^rd order at 1 station were determined as the optimal equations. Since the 1^st order equation is similar to the Sherman type equation and the 2^nd order one is similar to the general type equation, it was presented as a unified form of rainfall intensity equation for convenience of use by increasing the number of independent variables. Therefore, it is judged that there is no statistical problem in considering only the 3^rd order polynomial regression equation for the full-logged I-D-F.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.432-438
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• 2011

The purpose of the present thesis is to develop bankruptcy prediction models capable of being applied to the Korean construction industry and to deduce an optimal model through comparative evaluation of final developed models. A study population was selected as general contractors in the Korean construction industry. In order to ease the sample securing and reliability of data, it was limited to general contractors receiving external audit from the government. The study samples are divided into a bankrupt company group and a non-bankrupt company group. The bankruptcy, insolvency, declaration of insolvency, workout and corporate reorganization were used as selection criteria of a bankrupt company. A company that is not included in the selection criteria of the bankrupt company group was selected as a non-bankrupt company. Accordingly, the study sample is composed of a total of 112 samples and is composed of 48 bankrupt companies and 64 non-bankrupt companies. A financial ratio was used as early predictors for development of an estimation model. A total of 90 financial ratios were used and were divided into growth, profitability, productivity and added value. The MDA (Multivariate Discriminant Analysis) model and BLRA (Binary Logistic Regression Analysis) model were used for development of bankruptcy prediction models. The MDA model is an analysis method often used in the past bankruptcy prediction literature, and the BLRA is an analysis method capable of avoiding equal variance assumption. The stepwise (MDA) and forward stepwise method (BLRA) were used for selection of predictor variables in case of model construction. Twenty two variables were finally used in MDA and BLRA models according to timing of bankruptcy. The ROC-Curve Analysis and Classification Analysis were used for analysis of prediction performance of estimation models. The correct classification rate of an individual bankruptcy prediction model is as follows: 1) one year ago before the event of bankruptcy (MDA: 83.04%, BLRA: 93.75%); 2) two years ago before the event of bankruptcy (MDA: 77.68%, BLRA: 78.57%); 3) 3 years ago before the event of bankruptcy (MDA: 84.82%, BLRA: 91.96%). The AUC (Area Under Curve) of an individual bankruptcy prediction model is as follows. : 1) one year ago before the event of bankruptcy (MDA: 0.933, BLRA: 0.978); 2) two years ago before the event of bankruptcy (MDA: 0.852, BLRA: 0.875); 3) 3 years ago before the event of bankruptcy (MDA: 0.938, BLRA: 0.975). As a result of the present research, accuracy of the BLRA model is higher than the MDA model and its prediction performance is improved.

• Journal of the Korea Society of Computer and Information
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• v.23 no.8
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• pp.123-131
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• 2018

The purpose of this study is to investigate the clinical nursing competence and self-efficacy of 4th and 2nd semester nursing college students who completed most of the performance-based nursing education curriculum. It was attempted to develop competency evaluation and competency-based curriculum. The collected data were analyzed using descriptive statistics, t-test, one-way ANOVA, $scheff{\bar{e}}$ test, Pearson's correlation coefficients and Stepwise multiple regression in SPSS WIN 24.0 program. The findings of this study were as follows. 1) The knowledge level of essential basic nursing skills received a score of 88.95. The overall average score of clinical performance was 3.15 out of 5. The mean score of self-efficacy was $4.14{\pm}0.57$ points on 6 points 2) Among the general characteristics of subjects, 'motivation of major selection' and 'satisfaction of practice time' differed in the knowledge of essential basic nursing skills, 'religion' and 'health status' differed in clinical performance ability and 'interpersonal relationship', 'motivation of major selection', 'major satisfaction', 'satisfaction of practice time', 'nursing satisfaction', 'desired working period' and 'average rating' differed in self-efficacy. 3) The self-efficacy showed a significant positive correlation with the clinical nursing competency including the knowledge of essential basic nursing skills and clinical performance ability. The nursing satisfaction, clinical performance ability, the knowledge of essential basic nursing skills, interpersonal relationship and average rating influenced significantly and explained 23.7% of the subjects' self-efficacy.