• Title/Summary/Keyword: 로지스틱 회귀모형

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로지스틱 회귀모형을 분석하기 위한 SPSS, SAS, STATA의 비교분석

  • Kim, Sun-Gwi;Jeong, Dong-Bin
    • Proceedings of the Korean Statistical Society Conference
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    • 2002.11a
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    • pp.287-292
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    • 2002
  • 최근 여러 분야에서 로지스틱 회귀에 대한 필요성과 그 응용이 급증하면서 이를 분석하기 위한 통계패키지가 많이 개발되어 사용되고 있다. 이 논문에서는 자료의 유형에 따라 활용할 수 있는 여러 형태의 로지스틱 회귀모형을 간단히 살펴보고, SPSS, SAS, STATA, MINITAB과 같은 통계패키지를 사용하여 로지스틱 회귀모형에 적용할 때 각각 다룰 수 있는 범위와 그 특징에 대해 다룬다.

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마코프 로지스틱 회귀모형을 이용한 강수 확률예측

  • Park, Jeong-Su
    • 한국데이터정보과학회:학술대회논문집
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    • 2006.04a
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    • pp.345-352
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    • 2006
  • 현 기상의 시점에서 강수 확률 예측을 위해 가장 적절한 모형은 공간적 종속성과 시간적 종속성을 고려한 모형이 선택되어져야 한다. 보통 마크프 연쇄 모형과 예보인자를 이용하는 회귀 모형이 모두 고려된 모형을 사용한다. 본 논문에서는 강수 형태를 세 개의 상태로 나눈 경우, 즉 맑은 경우, 흐린 경우, 비온 경우로 나누어 마코프 로지스틱 회귀모형을 세우고 강수확률을 예측 할 수 있도록 하였다. 또한 서울 지역의 강수 자료를 이용하여 기존의 마코프 회귀모형과 마코프 로지스틱 회귀모형을 서로 비교하여 실제적 적용 문제를 다루었다.

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Exploring interaction using 3-D residual plots in logistic regression model (3차원 잔차산점도를 이용한 로지스틱회귀모형에서 교호작용의 탐색)

  • Kahng, Myung-Wook
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.1
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    • pp.177-185
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    • 2014
  • Under bivariate normal distribution assumptions, the interaction and quadratic terms are needed in the logistic regression model with two predictors. However, depending on the correlation coefficient and the variances of two conditional distributions, the interaction and quadratic terms may not be necessary. Although the need for these terms can be determined by comparing the two scatter plots, it is not as useful for interaction terms. We explore the structure and usefulness of the 3-D residual plot as a tool for dealing with interaction in logistic regression models. If predictors have an interaction effect, a 3-D residual plot can show the effect. This is illustrated by simulated and real data.

Steal Success Model for 2007 Korean Professional Baseball Games (2007년 한국프로야구에서 도루성공모형)

  • Hong, Chong-Sun;Choi, Jeong-Min
    • The Korean Journal of Applied Statistics
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    • v.21 no.3
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    • pp.455-468
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    • 2008
  • Based on the huge baseball game records, the steal plays an important role to affect the result of games. For the research about success or failure of the steal in baseball games, logistic regression models are developed based on 2007 Korean professional baseball games. The analyses of logistic regression models are compared of those of the discriminant models. It is found that the performance of the logistic regression analysis is more efficient than that of the discriminant analysis. Also, we consider an alternative logistic regression model based on categorical data which are transformed from uneasy obtainable continuous data.

Variable Selection for Logistic Regression Model Using Adjusted Coefficients of Determination (수정 결정계수를 사용한 로지스틱 회귀모형에서의 변수선택법)

  • Hong C. S.;Ham J. H.;Kim H. I.
    • The Korean Journal of Applied Statistics
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    • v.18 no.2
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    • pp.435-443
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    • 2005
  • Coefficients of determination in logistic regression analysis are defined as various statistics, and their values are relatively smaller than those for linear regression model. These coefficients of determination are not generally used to evaluate and diagnose logistic regression model. Liao and McGee (2003) proposed two adjusted coefficients of determination which are robust at the addition of inappropriate predictors and the variation of sample size. In this work, these adjusted coefficients of determination are applied to variable selection method for logistic regression model and compared with results of other methods such as the forward selection, backward elimination, stepwise selection, and AIC statistic.

Estimation of Asymmetric Bell Shaped Probability Curve using Logistic Regression (로지스틱 회귀모형을 이용한 비대칭 종형 확률곡선의 추정)

  • 박성현;김기호;이소형
    • The Korean Journal of Applied Statistics
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    • v.14 no.1
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    • pp.71-80
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    • 2001
  • Logistic regression model is one of the most popular linear models for a binary response variable and used for the estimation of probability function. In many practical situations, the probability function can be expressed by a bell shaped curve and such a function can be estimated by a second order logistic regression model. However, when the probability curve is asymmetric, the estimation results using a second order logistic regression model may not be precise because a second order logistic regression model is a symmetric function. In addition, even if a second order logistic regression model is used, the interpretation for the effect of second order term may not be easy. In this paper, in order to alleviate such problems, an estimation method for asymmetric probabiity curve based on a first order logistic regression model and iterative bi-section method is proposed and its performance is compared with that of a second order logistic regression model by a simulation study.

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데이터마이닝을 위한 혼합 데이터베이스에서의 속성선택

  • Cha, Un-Ok;Heo, Mun-Yeol
    • Proceedings of the Korean Statistical Society Conference
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    • 2003.05a
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    • pp.103-108
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    • 2003
  • 데이터마이닝을 위한 대용량 데이터베이스를 축소시키는 방법 중에 속성선택 방법이 많이 사용되고 있다. 본 논문에서는 세 가지 속성선택 방법을 사용하여 조건속성 수를 60%이상 축소시켜 결정나무와 로지스틱 회귀모형에 적용시켜보고 이들의 효율을 비교해 본다. 세 가지 속성선택 방법은 MDI, 정보획득, ReliefF 방법이다. 결정나무 방법은 QUEST, CART, C4.5를 사용하였다. 속성선택 방법들의 분류 정확성은 UCI 데이터베이스에 주어진 Credit 승인 데이터베이스와 German Credit 데이터베이스를 사용하여 10층-교차확인 방법으로 평가하였다.

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Study on Detection Technique for Cochlodinium polykrikoides Red tide using Logistic Regression Model and Decision Tree Model (로지스틱 회귀모형과 의사결정나무 모형을 이용한 Cochlodinium polykrikoides 적조 탐지 기법 연구)

  • Bak, Su-Ho;Kim, Heung-Min;Kim, Bum-Kyu;Hwang, Do-Hyun;Unuzaya, Enkhjargal;Yoon, Hong-Joo
    • The Journal of the Korea institute of electronic communication sciences
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    • v.13 no.4
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    • pp.777-786
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    • 2018
  • This study propose a new method to detect Cochlodinium polykrikoides on satellite images using logistic regression and decision tree. We used spectral profiles(918) extracted from red tide, clear water and turbid water as training data. The 70% of the entire data set was extracted and used for model training, and the classification accuracy of the model was evaluated by using the remaining 30%. As a result of the accuracy evaluation, the logistic regression model showed about 97% classification accuracy, and the decision tree model showed about 86% classification accuracy.

Comparison of Multinomial Logit and Logistic Regression on Disability Pensioners' Characteristic (다범주 자료의 다항로짓 모형과 로지스틱 회귀모형 비교;장애연금 특성분석 중심으로)

  • Kim, Mi-Jung
    • The Korean Journal of Applied Statistics
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    • v.21 no.4
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    • pp.589-602
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    • 2008
  • This article studies on disability pensioners' characteristic with multinomial logit and logistic regression model. Seven factors are examined on whether each factor is reflected in degree of disability in the disability pension. By incorporating multinomial logit and logistic regression model, effectiveness and characteristic of the seven factors are investigated on the degree of disability. Result shows all the seven factors are significant on the degree of disability, while among the seven, five factors, age, sex, type of coverage, type of category, insured duration show a trend in degree of disability and the other two, cause of disability and class of standard monthly income are not effective on trend in degree of disability. Results from analyses might be useful for disability pension management.

An educational tool for binary logistic regression model using Excel VBA (엑셀 VBA를 이용한 이분형 로지스틱 회귀모형 교육도구 개발)

  • Park, Cheolyong;Choi, Hyun Seok
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.2
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    • pp.403-410
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    • 2014
  • Binary logistic regression analysis is a statistical technique that explains binary response variable by quantitative or qualitative explanatory variables. In the binary logistic regression model, the probability that the response variable equals, say 1, one of the binary values is to be explained as a transformation of linear combination of explanatory variables. This is one of big barriers that non-statisticians have to overcome in order to understand the model. In this study, an educational tool is developed that explains the need of the binary logistic regression analysis using Excel VBA. More precisely, this tool explains the problems related to modeling the probability of the response variable equal to 1 as a linear combination of explanatory variables and then shows how these problems can be solved through some transformations of the linear combination.