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.

An Analysis of Factors Relating to Agricultural Machinery Farm-Work Accidents Using Logistic Regression

( Byoung Gap Kim ),( Sung Hyun Yum ),( Yu Yong Kim ),( Namkyu Yun ),( Seung Yeoub Shin ),( Seok Cheol You ) 한국농업기계학회 2014 바이오시스템공학 Vol.39 No.3

Purpose: In order to develop strategies to prevent farm-work accidents relating to agricultural machinery, influential factors were examined in this paper. The effects of these factors were quantified using logistic regression. Methods: Based on the results of a survey on farm-work accidents conducted by the National Academy of Agricultural Science, 21 tentative independent variables were selected. To apply these variables to regression, the presence of multicollinearity was examined by comparing correlation coefficients, checking the statistical significance of the coefficients in a simple linear regression model, and calculating the variance inflation factor. A logistic regression model and determination method of its goodness of fit was defined. Results: Among 21 independent variables, 13 variables were not collinear each other. The results of a logistic regression analysis using these variables showed that the model was significant and acceptable, with deviance of 714.053. Parameter estimation results showed that four variables (age, power tiller ownership, cognizance of the government`s safety policy, and consciousness of safety) were significant. The logistic regression model predicted that the former two increased accident odds by 1.027 and 8.506 times, respectively, while the latter two decreased the odds by 0.243 and 0.545 times, respectively. Conclusions: Prevention strategies against factors causing an accident, such as the age of farmers and the use of a power tiller, are necessary.

Comparative Study on Statistical Packages for Analyzing Logistic Regression - MINITAB, SAS, SPSS, STATA -

Kim, Soon-Kwi,Jeong, Dong-Bin,Park, Young-Sool 한국데이터정보과학회 2004 한국데이터정보과학회지 Vol.15 No.2

Recently logistic regression is popular in a variety of fields so that a number of statistical packages are developed for analyzing the logistic regression. This paper briefly considers the several types of logistic regression models used depending on different types of data. In addition, when four statistical packages (MINTAB, SAS, SPSS and STATA) are used to apply logistic regression models to the real fields respectively, their scope and characteristics are investigated.

Comparative Study on Statistical Packages for Analyzing Logistic Regression

Soon Kwi Kim,Dong Bin Jeong,Young Sool Park 한국데이터정보과학회 2004 한국데이터정보과학회지 Vol.15 No.2

Recently logistic regression is popular in a variety of fields so that a number of statistical packages are developed for analyzing the logistic regression. This paper briefly considers the several types of logistic regression models used depending on different types of data. In addition, when four statistical packages (MINTAB, SAS, SPSS and STATA) are used to apply logistic regression models to the real fields respectively, their scope and characteristics are investigated.

Biplots of Multivariate Data Guided by Linear and/or Logistic Regression

Huh, Myung-Hoe,Lee, Yonggoo The Korean Statistical Society 2013 Communications for statistical applications and me Vol.20 No.2

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.

Comparative study on statistical packages for analyzing logistic regression - MINITAB, SAS, SPSS, STATA -

김순귀,정동빈,박영술 한국데이터정보과학회 2004 한국데이터정보과학회지 Vol.15 No.2

Recently logistic regression is popular in a variety of fields so that a number of statistical packages are developed for analyzing the logistic regression. This paper briefly considers the several types of logistic regression models used depending on different types of data. In addition, when four statistical packages (MINTAB, SAS, SPSS and STATA) are used to apply logistic regression models to the real fields respectively, their scope and characteristics are investigated.

MULTIPLE OUTLIER DETECTION IN LOGISTIC REGRESSION BY USING INFLUENCE MATRIX

Lee, Gwi-Hyun,Park, Sung-Hyun The Korean Statistical Society 2007 Journal of the Korean Statistical Society Vol.36 No.4

Many procedures are available to identify a single outlier or an isolated influential point in linear regression and logistic regression. But the detection of influential points or multiple outliers is more difficult, owing to masking and swamping problems. The multiple outlier detection methods for logistic regression have not been studied from the points of direct procedure yet. In this paper we consider the direct methods for logistic regression by extending the $Pe\tilde{n}a$ and Yohai (1995) influence matrix algorithm. We define the influence matrix in logistic regression by using Cook's distance in logistic regression, and test multiple outliers by using the mean shift model. To show accuracy of the proposed multiple outlier detection algorithm, we simulate artificial data including multiple outliers with masking and swamping.

Multiple Outlier Detection in Logistic Regression by using Influence Matrix

Gwi Hyun Lee,박성현 한국통계학회 2007 Journal of the Korean Statistical Society Vol.36 No.4

Many procedures are available to identify a single outlier or an isolatedinuential point in linear regression and logistic regression. But the detectionof inuential points or multiple outliers is more dicult, owing to maskingand swamping problems. The multiple outlier detection methods for logisticregression have not been studied from the points of direct procedure yet. Inthis paper we consider the direct methods for logistic regression by extend-ing the Pe~na and Yohai (1995) inuence matrix algorithm. We dene theinuence matrix in logistic regression by using Cook's distance in logistic re-gression, and test multiple outliers by using the mean shift model. To showaccuracy of the proposed multiple outlier detection algorithm, we simulatearticial data including multiple outliers with masking and swamping.

Application of Crossover Analysis-logistic Regression in the Assessment of Gene- environmental Interactions for Colorectal Cancer

Wu, Ya-Zhou,Yang, Huan,Zhang, Ling,Zhang, Yan-Qi,Liu, Ling,Yi, Dong,Cao, Jia Asian Pacific Journal of Cancer Prevention 2012 Asian Pacific journal of cancer prevention Vol.13 No.5

Background: Analysis of gene-gene and gene-environment interactions for complex multifactorial human disease faces challenges regarding statistical methodology. One major difficulty is partly due to the limitations of parametric-statistical methods for detection of gene effects that are dependent solely or partially on interactions with other genes or environmental exposures. Based on our previous case-control study in Chongqing of China, we have found increased risk of colorectal cancer exists in individuals carrying a novel homozygous TT at locus rs1329149 and known homozygous AA at locus rs671. Methods: In this study, we proposed statistical method-crossover analysis in combination with logistic regression model, to further analyze our data and focus on assessing gene-environmental interactions for colorectal cancer. Results: The results of the crossover analysis showed that there are possible multiplicative interactions between loci rs671 and rs1329149 with alcohol consumption. Multifactorial logistic regression analysis also validated that loci rs671 and rs1329149 both exhibited a multiplicative interaction with alcohol consumption. Moreover, we also found additive interactions between any pair of two factors (among the four risk factors: gene loci rs671, rs1329149, age and alcohol consumption) through the crossover analysis, which was not evident on logistic regression. Conclusions: In conclusion, the method based on crossover analysis-logistic regression is successful in assessing additive and multiplicative gene-environment interactions, and in revealing synergistic effects of gene loci rs671 and rs1329149 with alcohol consumption in the pathogenesis and development of colorectal cancer.

의사결정나무분석과 로지스틱 회귀분석을 이용한 중학생자살생각 예측요인 비교연구

권영란 한국자료분석학회 2010 Journal of the Korean Data Analysis Society Vol.12 No.6

The purpose of this study was to identify factors which predict suicidal ideation on middle school students using Decision Tree and Logistic Regression. The subjects were 680 (M=327, F=353) from middle school students in G city. Data were collected from March, 16 to April, 2, 2010, and analyzed with the descriptive analysis, t-test, ANOVA, decision tree, logistic regression using SPSS/Win 15.0 program. The result of this research showed that 39.1% the subjects were found to be suicidal ideation. The common predicting variables of suicidal ideation on middle school students were depression, school adjustment, and satisfactory of school-life between the decision tree and logistic regression. As the difference of level of accuracy, decision tree was 75.6% and logistic regression showed 78.4%. Based on the above findings, it is recommended that comparison study on the impact of school adjustment and depression on suicidal ideation. 본 연구는 데이터마이닝 기법인 의사결정분석과 로지스틱 회귀분석을 활용하여 중학생 자살생각에 영향을 미치는 요인을 분류하고 예측하고자 시도되었다. 연구대상자는 일 도시지역의 중학생 680명이었다. 자료수집은 2010년 3월16일부터 4월2일까지였으며, 통계분석은 SPSS/Win 15.0 프로그램을 활용하여 기술통계, t-검정, ANOVA, 의사결정나무분석과 로지스틱 회귀분석을 실시하였다. 연구결과 연구대상자의 266명(39.1%)이 자살생각이 평균이상으로 분류되었다. 의사결정나무분석에서 중학생 자살생각이 있을 확률이 가장 높은 마디(81.0%)는 우울수준이 높고, 학교적응력이 낮으며, 학업성적이 보통이하인 경우로 조사되었다. 또한 의사결정나무분석과 로지스틱 회귀분석에서 공통적으로 나타난 중학생의 자살생각 예측요인으로는 우울, 학교적응력, 학교생활 만족도로 나타났다. 그러나 분류의 정확도는 의사결정나무분석의 경우 75.6%였으며, 로지스틱 회귀분석에서는 78.4%로 좀 더 높게 조사되었다.