임상의를 위한 다변량 분석의 실제

오주한(Joo Han Oh),정석원(Seok Won Chung) 대한견주관절의학회 2013 대한견주관절의학회지 Vol.16 No.1

임상 의학의 연구에 사용되는 대표적 다변량 분석 방법은 다중 회귀 분석 방법인데, 이는 인과 관계를 토대로 여러 개의 변수에 의한 한꺼번에의 영향력을 분석하기 위한 방법이다. 다중 회귀 분석은 기본적으로 회귀분석의 기본 가정을 만족해야 함은 물론, 여러 개의 독립 변수들이 포함되기 때문에 변수들을 모형에 포함시키는 방법 및 다중 공선성 문제에 대한 고려가 필요하다. 다중 회귀 분석 모형의 설명력은 결정 계수 R2으로 표현되어 1에 가까울수록 설명력이 크며, 각 독립 변수들의 결과에의 영향력은 회귀 계수인 β값으로 표현된다. 다중 회귀 분석은 종속 변수의 형태에 따라 다중 선형 회귀 분석, 다중 로지스틱 회귀 분석, 콕스 회귀 분석으로 나눌 수 있다. 종속 변수가 연속 변수인 경우 다중 선형 회귀 분석, 범주형 변수인 경우 다중 로지스틱 회귀 분석, 시간의 영향을 고려한 상태 변수인 경우는 콕스 회귀 분석을 시행해야 하며, 각각 결과에의 영향력은 회귀 계수 β, 교차비, 위험비로 평가한다. 이러한 다변량 분석에 대한 이해는 연구를 계획하고 결과를 분석하고자 하는 임상 의사에게 있어 보다 효율적인 연구를 위해 필수적인 소양이라고 할 수 있다. In medical research, multivariate analysis, especially multiple regression analysis, is used to analyze the influence of multiple variables on the result. Multiple regression analysis should include variables in the model and the problem of multi-collinearity as there are many variables as well as the basic assumption of regression analysis. The multiple regression model is expressed as the coefficient of determination, R2 and the influence of independent variables on result as a regression coefficient, β. Multiple regression analysis can be divided into multiple linear regression analysis, multiple logistic regression analysis, and Cox regression analysis according to the type of dependent variables (continuous variable, categorical variable (binary logit), and state variable, respectively), and the influence of variables on the result is evaluated by regression coefficient β, odds ratio, and hazard ratio, respectively. The knowledge of multivariate analysis enables clinicians to analyze the result accurately and to design the further research efficiently.

의사결정나무와 로지스틱 회귀분석을 이용한 태권도 수련생 이탈 예측을 위한 비교 연구

권태원(Kwon Tae-Won),구유희(Koo Yu-Hoe) 한국체육과학회 2008 한국체육과학회지 Vol.17 No.2

The purpose of this study is to suggest the most appropriate prediction model for prediction of secession of trainees of Taekwondo gymnasium through decision-making tree technique among logistics regression analysis and data mining techniques. In order to accomplish the purpose of this study, I have distributed 1,500 questionnaires sheets to the trainees using by convenience sampling method among non probability sampling extraction methods by setting trainees of Taekwondo gymnasium located in Gyeonggi-Do and Incheon Results of this study derived from this procedure and method are as follows. 1. As the result of examining the difference of level of specialty between decision-making tree technique & logistics regression analysis, in the level of specialty predicting carry forward number of people with actual number of carry-over, logistics regression analysis was 92.9% and decision-making tree technique showed a little higher at 96.3%. 2. As the result of examining the difference of level of sensitivity between decision-making tree technique & logistics regression analysis, in the level of sensitivity predicting carry forward number of people with actual number of carry-over, logistics regression analysis was 64.0% and decision-making tree technique showed 44.5%. 3. As the result of examining the difference of level of accuracy between decision-making tree technique & logistics regression analysis, in the level of accuracy predicting carry forward number of people with actual number of carry-over, logistics regression analysis was 86.8% and logistics regression analysis showed 87.6%. 4. As the results of examining variables affecting secession of trainees of Taekwondo gymnasium between decision-making tree technique and logistics regression analysis, in case of decision-making tree technique, variables were training period, grade, satisfaction for instructor, recommendation will, and satisfaction of facility and in case of logistics regression analysis, variables were training period, grade, recommendation will, satisfaction for instructor, and per sex. Summarizing the above result, as the result of comparison analysis of secession prediction of trainees of Taekwondo gymnasium between decision-making tree technique and logistics regression analysis, the two models showed all high prediction rate in the level of accuracy and there was no difference of prediction rate between the two analysis models. This result is believed to be because the same variables affecting secession were used in the same.

체육학에서의 통계적 연구: 회귀분석을 중심으로

정연택,최연재 인문사회 21 2022 인문사회 21 Vol.13 No.3

체육학에서의 통계적 연구: 회귀분석을 중심으로정 연 택*ㆍ최 연 재** 요약: 통계적 연구 방법이 발전하면서 회귀분석을 체육학에서 사용하는 빈도가 높아지고 있다. 회귀분석은 상관분석에 비해 세밀한 연구 문제를 탐색할 수 있는 장점이 있다. 그리고 타당한 결론을 얻기 위해 회귀분석은 변수 선정이 무엇보다 중요하다. 회귀분석의 오류와 주의할 점을 고려하여 1장에서는 회귀분석의 역사와 개념에 관해서 서술하였으며, 2장은 회귀분석의 기본 가정 중, 상관관계와 다중공선성에 관한 기술이 이루어졌다. 3장에서는 다중회귀분석의 추정 방식을 분석하였으며, 4장에서는 최소자승법, 표준화 회귀계수, 비표준화 회귀계수의 중요도를 설명하였다. 5장에서는 단순회귀모형과 중다회귀모형에 대하여 서술하였으며, 6장은 앞서 기술한 회귀분석을 바탕으로 논의가 이루어졌다. 그리고 7장에서는 회귀분석의 활성화를 위한 방안을 제시하였다. 핵심어: 체육학, 통계적 연구, 회귀분석, 다중공선성, 상관분석 □ 접수일: 2022년 5월 11일, 수정일: 2022년 6월 6일, 게재확정일: 2022년 6월 20일* 주저자, 영남대학교 사범대학 특수체육교육과 교수. (First Author, Professor, Yeungnam Univ., Email: jyt7872@yu.ac.kr)** 교신저자, 영남대학교 사범대학 특수체육교육과 강사. (Corresponding Author, Lecturer, Yeungnam Univ., Email: 20802191@yu.ac.kr) Statistical Research Methods in Physical Education:Focusing on Regression AnalysisYeontaek Jeong & Yeonjae Choi Abstract: Development of statistical research methods, the frequency of using regression analysis in physical education is increasing. Regression analysis has the advantage of being able to explore detailed research problems compared to correlation analysis. And to obtain valid conclusions, the selection of variables in regression analysis is of paramount importance. In consideration of errors and cautions in regression analysis, Chapter 1 describes the history and concept of regression analysis, and Chapter 2 describes correlation and multicollinearity among the basic assumptions of regression analysis. Chapter 3 analyzed the estimation method of multiple regression analysis, and Chapter 4 explained the importance of least squares method, standardized beta coefficient, and non-standardized regression coefficient. Chapter 5 described the simple regression model and multiple regression model, and Chapter 5 discussed the regression analysis described above. And in Chapter 6 presented a way to activate regression analysis. Key Words: Physical Education, Statistical Research Methods, Regression Analysis, Multicollinearity, Correlation Analysis

The Research of Multiple Regression Analysis in Rural-Urban Income Disparity

Jian Li,Xiangyu Guo 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.11

The multiple linear regression model contains more than one predictor variable and it shows the relationship among multiple variables. In the existing research field of rural-urban income disparity, the method of multiple regression analysis is mainly employed. But the linear relationship among variables is estimated mainly depending on principal component analysis. Principal component analysis is used to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The principal component analysis is widely used for feature extraction to reveal the most main factors from the multiple aspects. A multiply linear regression model integrating principal components analysis is proposed to address on the income gap between the city and country. The influential factors are given and the analysis results are discussed in this paper. The experimental results on income data from 1990 to 2013 show that the proposed method is effective in predicting the income ratio and analyzing the influential factors.

의사결정나무분석과 로지스틱 회귀분석을 이용한 우울 예측요인 비교연구

윤지선 ( Youn Ji Sun ) 한국사회복지경영학회 2020 사회복지경영연구 Vol.7 No.2

본 연구는 노년기 삶의 질을 저해하는 우울증에 대한 관심으로부터 수행되었다. 의사결정나무(decision tree) 분석을 활용하여 노인의 우울 요인을 분류 및 예측하고, 이를 로지스틱 회귀분석 결과와 비교하여 예측 정확성을 정의하는 서술적 조사연구이다. 연구대상자는 국민연금연구원의 국민노후보장패널(KReIS) data 중, 7차 개인조사에 참가한 65세 이상 노인 총 2,096명이다. 자료분석은 SPSS 23.0 프로그램을 이용하여 기술통계, 교차분석, Roc Curve, 의사결정나무 분석, 로지스틱 회귀분석을 하였다. 연구결과, 의사결정나무 분석에서 우울 예측요인은 일상 및 사회생활 제한과 주관적 경제 불만족으로 나타났다. 로지스틱 회귀분석에서는 일상 및 사회생활 제한과 주관적 경제 불만족, 대인관계 불만족으로 나타났다. 노인의 우울에 영향을 미치는 예측력을 로지스틱 회귀분석과 의사결정나무 분석을 통해 비교한 결과, 우울을 예측하는 민감도는 로지스틱 회귀분석(44.4%)이 의사결정나무 분석(33.6%) 보다 높게 나타났다. 또한 실제 우울을 예측하는 특이도는 의사결정나무 분석(91.9%)이 로지스틱 회귀분석(86.3%) 보다 높은 것으로 나타났다. 분류정확도는 로지스틱 회귀분석(71.6%)이 의사결정나무 분석(71.4%)보다 조금 높게 나타났다. 연구결과를 기초로 두 기법의 예측 및 분류도 구로서의 유용성 판단은 민감도와 분류 정확도가 더 높게 나타난 로지스틱 회귀분석방법이 노인의 우울 예측모형을 구축하는데 더 유용한 자료로 사용될 수 있을 것으로 사료된다. 반면, 의사결정나무 분석은 분석의 정확도보다는 분석과정의 특정 경로설명이 필요한 경우에 유용하게 사용될 수 있을 것으로 보인다. This study was carried out from the interest in depression, which undermines the quality of life in old age, which has been extended by life expectancy. It is a descriptive investigation study that utilizes decision tree analysis with data mining technique to classify and predict depression factors of the elderly, and compare them with logistic regression results to define prediction accuracy. Among the data of the National Pension Research Institute's Korea National Age Security Panel(KReIS), a total of 2,096 senior citizens aged 65 or older participated in the seventh personal survey conducted in 2017. The data analysis was performed using the SPSS 23.0 program, including technical statistics, cross-analysis, logistic regression, Loc Curve, and decision tree analysis. The results of the study showed that the factors of depression prediction in decision tree analysis were daily and social life restriction and subjective economic dissatisfaction. Logistic regression showed limitations in daily and social life, subjective economic dissatisfaction and interpersonal dissatisfaction. Comparing the predictive power that affects the depression of the elderly through logistic regression and decision tree analysis, the sensitivity of predicting depression was higher than that of the decision tree(33.6%). In addition, the specificity of predicting actual depression was higher than that of logistic regression(86.3%) with decision tree analysis(91.9%). Classification accuracy was slightly higher than logistic regression(71.6%) in decision tree analysis(71.4%). Based on the results of the study, it is estimated that the logistic regression method, which shows higher sensitivity and accuracy of classification, can be used as more useful data to build a depression prediction model for the elderly. On the other hand, decision tree analysis may be useful when specific path descriptions of the analysis process are needed rather than the accuracy of the analysis.

Survival analysis: part II – applied clinical data analysis

인준용,이동규 대한마취통증의학회 2019 Korean Journal of Anesthesiology Vol.72 No.5

As a follow-up to a previous article, this review provides several in-depth concepts regarding a survival analysis. Also, several codes for specific survival analysis are listed to enhance the understanding of such an analysis and to provide an applicable survival analysis method. A proportional hazard assumption is an important concept in survival analysis. Validation of this assumption is crucial for survival analysis. For this purpose, a graphical analysis method and a goodnessof- fit test are introduced along with detailed codes and examples. In the case of a violated proportional hazard assumption, the extended models of a Cox regression are required. Simplified concepts of a stratified Cox proportional hazard model and time-dependent Cox regression are also described. The source code for an actual analysis using an available statistical package with a detailed interpretation of the results can enable the realization of survival analysis with personal data. To enhance the statistical power of survival analysis, an evaluation of the basic assumptions and the interaction between variables and time is important. In doing so, survival analysis can provide reliable scientific results with a high level of confidence.

생활무용 참여자의 지속행동에 미치는 영향요인에 관한 위계적 회귀분석

김보람 한국리듬운동학회 2019 한국리듬운동학회지 Vol.12 No.2

The purpose of this study is to investigate the factors affecting the continuous behavior of dance-for-all participants through regression analysis. For study subject, middle-aged women participating in the dance-for-all in the region of Gangwon-do were set as the population, and a total of 355 copies was collected and 317 copies were used in the final analysis. Data processing was conducted using SPSS Windows ver 22.0 program to perform frequency analysis, exploratory factor analysis, reliability analysis, correlation analysis, and hierarchical regression analysis. As the results of the analysis, first, in stage 1 of Hierarchical Regression Analysis, continuous behavior was shown to become heightened the longer the participation period in the dance-for-all had been. Second, in stage 2 of Hierarchical Regression Analysis, psychological satisfaction, relaxation & physiological satisfaction, and environmental satisfaction among the subfactors of leisure satisfaction were shown to have positive effects on continuous behavior, however, participation period was excluded from the explanatory variable. Third, in stage 3 of Hierarchical Regression Analysis, psychological satisfaction, relaxation & physiological satisfaction, environmental satisfaction, and exercise efficacy of self-efficacy were shown to have positive effects on continuous behavior. Further, it was confirmed that relaxation & physiological satisfaction is the most important factor. For Middle-aged women participating in the dance-for all, feeling healthy and being satisfied with relaxation is likely linked with the will and behavior that lead to continuous participation in the future above any other educational or social values. From now on, participating in the dance-for-all will be established as an even meaningful leisure activity and lifetime sport for the middle-aged women.

Shapley value regression을 활용한 한국남자프로농구의 경기력 요인 분석

김봉석,최경호 한국스포츠학회 2018 한국스포츠학회지 Vol.16 No.1

프로스포츠의 경기력 관련 요인을 탐색함에 있어 그동안 많이 활용된 방법 중의 하나가 바로 회귀분석이다. 그런 데 회귀분석은 모형예측 등을 수행함에 있어서, 설명변수들에 대하여 독립성 가정이 요구되는 등 통계적인 측면에서 많 은 문제점이 있는 방법이기도 하다. 따라서 회귀분석을 실제로 적용함에 있어서는 이러한 문제점을 극복할 수 있는 대안 이 요구되는 바, 본 연구에서는 그 대안으로 Shapley value regression을 활용할 것을 제언하였다. 이를 위해 2016~2017 한국남자프로농구 데이터를 이용하여 Shapley value regression을 이용한 상대적 중요도를 구해 보았 다. 그 결과 회귀분석에서는 통계적으로 유의하지 않았던 ‘어시스트’나 ‘3점 성공률’ 그리고 수비 관련 변수에서는 ‘GD’ 등의 변수가 나름 상대적으로 중요한 것으로 나타났다. 본 연구는 프로농구 자료에 대하여 경기력에 영향을 미치는 변수 를 탐색함에 있어 기존의 회귀분석과는 다른 접근을 통해 보았다는점에서 의의가 있으며, 향후 프로스포츠 관련 양적 연구를 수행하는 연구자들에게 보다 정확한 결과도출 및 해석을 하는데 도움을 줄 수 있을 것으로 설명 할 수 있다. Regression analysis is one of the most popular ways to explore the performance related factors of professional sports. Regression analysis is a method that has many problems in terms of statistics, such as the need for an independent assumption about the described variables, in carrying out model predictions, etc. Therefore, when applying regression analysis in practice, alternatives to overcome these problems are needed, and the study used the Shapley value regression suggestion. To this end, data from the Korean Basketball League from 2016 to 2017 is used to identify the relative importance using Shapley value regression. As a result, offense-related variables such as ‘assists’ or ‘three-point field goal percentage’ and a defense-related variable including ‘GD’ were found to be relatively important, which were not statistically significant when using regression analysis. This study is meaningful in that it explores variables affecting the performance of professional basketball through a different approach from the existing regression analysis. It can also help researchers who perform quantitative research related to professional sports in the future to derive and interpret more accurate results.

Correlation Between Tractor Variables and Loan Support Limit in South Korea Through Regression Analysis

황석준,김정훈,장문경,남주석 한국농업기계학회 2022 바이오시스템공학 Vol.47 No.3

Purpose The correlation between the major variables of tractors and the loan support limit was investigated through regression analysis. Methods The loan support limit according to engine power,weight, engine displacement, width, length, and height was surveyed for 118 tractors commercially available in South Korea. Simple linear regression analysis was performed to understand the effects of the individual variables on the loan support limit. Furthermore, the major variables with a high correlation with the loan support limit were selected through Pearson correlation analysis, and multiple linear regression analysis was performed. Results Simple regression models and multiple regression models were derived to predict the tractor loan support limit. The coefficient of determination and the root mean square error were calculated to determine the accuracy of each regression model. In the simple linear regression analysis, the coefficient of determination of the engine-power-based regression model was the highest (0.87), followed by weight, engine displacement, width, length, and height. Similarly, the root mean square error was the smallest in the engine-power-based regression model at 3,770,370 KRW. As a result of performing multiple linear regression analysis using engine power and weight, which exhibited a correlation coefficient of 0.8 or higher in Pearson correlation analysis, the coefficient of determination and the root mean square error were 0.88 and 3,699,940 KRW, respectively. Conclusion As the multiple regression model with engine power and weight as variables has a high coefficient of determination and small root mean square error, it is considered the most suitable for predicting the tractor loan support limit. The developed prediction model can save time and greatly help the decision-making process of farmers for purchasing agricultural tractors.

미세먼지(PM₁₀) 오염농도와 토지피복간의 상관성 분석을 통한 GWR 모델의 적합성 평가

류형원 ( Ryu Hyeong-won ),장동호 ( Jang Dong-ho ) 한국사진지리학회 2019 한국사진지리학회지 Vol.29 No.1

본 연구는 미세먼지(PM₁₀) 오염농도와 토지피복간의 상관성 분석을 통해 GWR(Geographically Weighted Regression) 모델의 적합성 평가를 실시하였다. 특히, 미세먼지 특성상 공간 자기상관성이 있다고 판단되어 동일한 회귀 분석 모형에 일반최소자승법과 지리가중회귀분석 모델을 적용하여 결과를 비교하였다. 연구결과, 두 회귀분석 모델의 결과를 비교했을 때 지리가중회귀분석 모델의 결정계수 값이 높아 모형 설명력이 더 높은 것으로 나타났다. 또한 AICc(Akaike Information Criterion correction) 계수는 일반최소자승법보다 지리가중회귀분석에서 더 낮은 값을 보여 지리가중회귀분석을 통한 모델의 개선이 이루어졌다고 판단된다. Moran 지수를 이용하여 두 모델의 표준잔차에 대한 공간 자기상관성을 비교 분석한 결과, 일반최소자승법의 표준잔차에서는 정적 상관관계가 나타나 공간 자기상관성이 뚜렷하게 나타났지만, 지리가중회귀분석의 표준잔차에서는 무작위 패턴이 나타나 공간자기상관성을 모두 통제한 것으로 나타났다. 본 연구는 미세먼지 관련 연구에 있어 지리가중회귀분석의 적합성을 밝히고, 분석을 통해 얻어진 결과로 미세먼지 관련 연구에 기초데이터를 제공함으로써 국내 미세먼지 오염농도 저감에 기여하고자 하였다. This study was conducted to evaluate the suitability of the GWR model for the correlation analysis between fine particles(PM10) pollution concentration and land-cover. Especially, it was judged that there is spatial autocorrelation due to the characteristics of fine particles, and the results are compared by applying the ordinary least squares method and the geographically weighted regression analysis model to the same regression analysis model. As a result of the study, comparison of two regression analysis the geographically weighted regression model shows higher explanatory power. The Akaike information criterion correction(AICc) coefficient was also lower in the geographically weighted regression analysis than the ordinary least squares method. Therefore, it is considered that the model was improved through the geographically weighted regression analysis. The Moran index was used to compare the spatial autocorrelation of the standard residuals of the two models. In the standard residual of ordinary least squares method, static correlation appeared and spatial autocorrelation appeared. However, in the standard residuals of the geographically weighted regression analysis, random patterns appeared to control all spatial autocorrelation. This study showed that the suitability of geographically weighted regression analysis in the study of fine particles. As a result of the analysis, it was aimed to contribute to the reduction of the fine particles pollution concentration in Korea by providing basic data for the study of fine particles.