• 한국환경과학회지
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• 제14권11호
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• pp.1049-1056
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• 2005

[ $NO_2$ ] concentration characteristics of Busan metropolitan city was analysed by statistical method using hourly $NO_2$ concentration data$(1998\~2000)$ collected from air quality monitoring sites of the metropolitan city. 4 representative regions were selected among air quality monitoring sites of Ministry of environment. Concentration data of $NO_2$, 5 air pollutants, and data collected at AWS was used. Both Stepwise Multiple Regression model and ARIMA model for prediction of $NO_2$ concentrations were adopted, and then their results were compared with observed concentration. While ARIMA model was useful for the prediction of daily variation of the concentration, it was not satisfactory for the prediction of both rapid variation and seasonal variation of the concentration. Multiple Regression model was better estimated than ARIMA model for prediction of $NO_2$ concentration.

• 한국산학기술학회논문지
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• 제12권11호
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• pp.4764-4769
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• 2011

본 연구는 종합병원 간호사의 조직몰입과 관련 요인을 파악하기 위하여 시도되었다. 자료는 SPSS 19.0 Version으로 기술적 통계, t-test, ANOVA, Scheffe's test, Pearson correlation coeffcient, Stepwise multiple Regression을 이용하여 분석하였다. 간호사의 조직몰입은 재직기간, 결혼유무, 월수입, 성격, 밤번횟수에 따라 통계적으로 유의한 차이가 있었다. 간호사의 조직몰입은 직무만족, 소진과 유의한 상관관계가 있는 것으로 나타났다. 조직몰입에 영향을 미치는 변수로는 소진이 가장 높은 설명력을 나타냈으며, 직무만족, 밤번근무횟수가 유의한 변수로 모두 48.6%의 설명력을 나타내었다. 그러므로 간호사의 조직몰입을 향상시키기 위해 소진을 낮추고, 직무만족도를 높이고, 밤번 횟수를 줄이는 것이 필요하다.

• 한국작물학회지
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• 제50권4호
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• pp.268-275
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• 2005

Early predictions of crop yields call provide information to producers to take advantages of opportunities into market places, to assess national food security, and to provide early food shortage warning. The objectives of this study were to identify the most useful parameters for estimating yields and to compare two model selection methods for finding the 'best' model developed by multiple linear regression. This research was conducted in two 65ha corn/soybean rotation fields located in east central South Dakota. Data used to develop models were small temporal variability information (STVI: elevation, apparent electrical conductivity $(EC_a)$, slope), large temporal variability information (LTVI : inorganic N, Olsen P, soil moisture), and remote sensing information (green, red, and NIR bands and normalized difference vegetation index (NDVI), green normalized difference vegetation index (GDVI)). Second order Akaike's Information Criterion (AICc) and Stepwise multiple regression were used to develop the best-fitting equations in each system (information groups). The models with $\Delta_i\leq2$ were selected and 22 and 37 models were selected at Moody and Brookings, respectively. Based on the results, the most useful variables to estimate corn yield were different in each field. Elevation and $EC_a$ were consistently the most useful variables in both fields and most of the systems. Model selection was different in each field. Different number of variables were selected in different fields. These results might be contributed to different landscapes and management histories of the study fields. The most common variables selected by AICc and Stepwise were different. In validation, Stepwise was slightly better than AICc at Moody and at Brookings AICc was slightly better than Stepwise. Results suggest that the Alec approach can be used to identify the most useful information and select the 'best' yield models for production fields.

• 대한간호학회지
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• 제33권5호
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• pp.625-632
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• 2003

Purpose: The purpose of this study was to assess the importance and sensitivity to nursing interventions of four nursing sensitive nursing outcomes selected from the Nursing Outcomes Classification (NOC). Outcomes for this study were 'Knowledge: Diet', 'Knowledge: Disease Process', 'Knowledge: Energy Conservation', and 'Knowledge: Health Behaviors'. Method: Data were collected from 183 nurses working in 2 university hospitals. Fehring method was used to estimate outcome and indicators' content and sensitivity validity. Multiple and stepwise regression were used to evaluate relationships between each outcome and its indicators. Result: Results confirmed the importance and nursing sensitivity of outcomes and their indicators. Key indicators of each outcomes were found by multiple regression. 'Knowledge: Diet' was suggested for adding new indicators because the variance explained by indicators was relatively low. Not all of the indicators selected for stepwise regression model were rated for highly in Fehring method. The R² statistics of the stepwise regression models were between 18 and 63% in importance by selected indicators and between 34 and 68% in contribution by selected indicators. Conclusion: This study refined what outcomes and indicators will be useful in clinical practice. Further research will be required for the revision of outcome and indicators of NOC. However, this study refined what outcomes and indicators will be useful in clinical practice.

• 한국운동역학회지
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• 제26권4호
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• pp.353-360
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• 2016

Objective: The aim of this study was to investigate the relationship between aiming patterns and scores in archery shooting. Method: Four (N = 4) elementary-level archers from middle school participated in this study. Aiming pattern was defined by averaged acceleration data measured from accelerometers attached on the body during the aiming phase in archery shooting. Stepwise multiple regression analysis was used to test whether a model incorporating aiming patterns from all nine accelerometers could predict the scores. In order to extract period of interest (POI) data from raw data, a Dynamic Time Warping (DTW)-based extraction method was presented. Results: Regression models for all four subjects are conducted with different significance levels and variables. The significance levels of the regression models are 0.12%, 1.61%, 0.55%, and 0.4% respectively; the $R^2$ of the regression models is 64.04%, 27.93%, 72.02%, and 45.62% respectively; and the maximum significance levels of parameters in the regression models are 1.26%, 4.58%, 5.1%, and 4.98% respectively. Conclusion: Our results indicated that the relationship between aiming patterns and scores was described by a regression model. Analysis of the significance levels, variables, and parameters of the regression model showed that our approach - regression analysis with DTW - is an effective way to raise scores in archery shooting.

• 한국병원경영학회지
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• 제26권2호
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• pp.17-26
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• 2021

Purposes: This study aims to identify factors affecting dental university hospitals' profitability and understand recent their business condition. Methodology: Data from 2016 to 2019 was collected from financial statement, public open data in 8 dental university hospitals. For the study, multiple regression test with stepwise selection was applied. Findings: First of all, 9 out of 19 independent variables were selected by stepwise selection. As a result of multiple regression test with selected independent variables and the dependent variable(operating profit margin ratio), the factors affecting hospitals' profitability were the number of dental unit chair, hospital location, debt ratio, total capital turnover ratio, employment cost rate, material cost rate, management expense rate, the number of patient per a dentist. Practical Implication: To improve dental university hospitals' profitability, hospitals specifically analysis and manage their cost such as employment, material and management cost and seek effectiveness by managing the proper number of patient per a dentist.

• 응용통계연구
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• 제26권6호
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• pp.915-922
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• 2013

분위수 회귀는 반응변수의 조건부 분위수 함수를 추정함으로써 반응변수와 예측변수의 관계에 대한 포괄적인 정보를 제공한다. 그러나 여러 개의 분위수 함수를 개별적으로 추정하게 되면 이들이 서로 교차할 가능성이 있으며, 이러한 분위수 함수의 교차(quantile crossing) 현상 분위수의 이론적 기본 특성에 위배된다. 본 논문에서는 다중 비교차 분위수 함수의 추정을 위해 커널 계수에 제약식을 부여하는 순차적 추정법을 제안하였으며, 모의실험을 통해 제안한 방법론의 효율적인 성능과 유용성을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2015년도 제46회 하계학술대회
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• pp.475-476
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• 2015

전력수요는 다양한 외부요인으로부터 영향을 받으므로 전력수요 예측 시 각 요인과의 상관관계를 고려할 필요가 있다. 본 논문은 Stepwise 다중회귀분석법을 이용한 일일 최대전력수요 예측 방법을 제시하였다. 사례연구에서는 2014년 평일 전력수요데이터를 이용하여 제안된 예측방법을 적용하고 그 결과를 평가하였다.

• 대한산업공학회지
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• 제42권5호
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• pp.314-326
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• 2016

The purpose of variable selection techniques is to select a subset of relevant variables for a particular learning algorithm in order to improve the accuracy of prediction model and improve the efficiency of the model. We conduct an empirical analysis to evaluate and compare seven well-known variable selection techniques for multiple linear regression model, which is one of the most commonly used regression model in practice. The variable selection techniques we apply are forward selection, backward elimination, stepwise selection, genetic algorithm (GA), ridge regression, lasso (Least Absolute Shrinkage and Selection Operator) and elastic net. Based on the experiment with 49 regression data sets, it is found that GA resulted in the lowest error rates while lasso most significantly reduces the number of variables. In terms of computational efficiency, forward/backward elimination and lasso requires less time than the other techniques.

• 디지털융복합연구
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• 제14권11호
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• pp.597-605
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• 2016

본 연구는 중년여성의 건강보존에 미치는 융합적 요인들을 확인하고자 시도된 연구이다. 연구대상자는 40세~59세의 중년여성 143명이며 자료수집기간은 2016년 6월 1일부터 6월 25일까지였다. 자료는 기술통계, t-test, ANOVA, Pearson's correlation coefficients, stepwise multiple regression을 이용하여 분석하였다. 건강보존과 건강증진 행위간의 관계를 보면 통계적으로 유의한 상관관계가 있는 것으로 나타났으며(r=.353, p<.001), 갱년기증상(r=-.062, p=.465)과 지혜(r=.120, p=.153)는 건강보존과 상관관계가 없는 것으로 나타났다. 다중회귀분석 결과 건강증진행위는 12.5%(${\beta}=.348$, p<.001), 배우자유무는 3.2%(${\beta}=.181$, p=.021)로 중년여성의 건강보존에 연관성이 있었다. 본 연구는 중년여성의 건강보존 및 증진을 위한 기초자료를 제공했음에 의의가 있다.