• 제목/요약/키워드: regression factor

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의사방문수 결정요인 분석 (A Study on Factors Affecting the Use of Ambulatory Physician Services)

  • 박현애;송건용
    • 보건행정학회지
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    • 제4권2호
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    • pp.58-76
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    • 1994
  • In order to study factors affecting the use of the ambulatory physician services. Andersen's model for health utilization was modified by adding the health behavior component and examined with three different approaches. Three different approaches were the multiople regression model, logistic regression model, and LISREL model. For multiple regression, dependent variable was reported illness-related visits to a physician during past one year and independent variables are variaous variables measuring predisposing factor, enabling factor, need factor and health behavior. For the logistic regression, dependent variable was visit or no-visit to a physician during past one year and independent variables were same as the multiple regression analysis. For the LISREL, five endogenous variables of health utiliztion, predisposing factor, enabling factor, need factor, and health behavior and 20 exogeneous variables which measures five endogenous variables were used. According to the multiple regression analysis, chronic illness, health status, perceived health status of the need factor; residence, sex, age, marital status, education of the predisposing factor ; health insurance, usual source for medical care of enabling factor were the siginificant exploratory variables for the health utilization. Out of the logistic regression analysis, health status, chronic illness, residence, marital status, education, drinking, use of health aid were found to be significant exploratory variables. From LISREL, need factor affect utilization most following by predisposing factor, enabling factor and health behavior. For LISREL model, age, education, and residence for predisposing factor; health status, chronic illess, and perceived health status for need factor; medical insurance for enabling factor; and doing any kind of health behavior for the health behavior were found as the significant observed variables for each theoretical variables.

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Multicollinarity in Logistic Regression

  • Jong-Han lee;Myung-Hoe Huh
    • Communications for Statistical Applications and Methods
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    • 제2권2호
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    • pp.303-309
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    • 1995
  • Many measures to detect multicollinearity in linear regression have been proposed in statistics and numerical analysis literature. Among them, condition number and variance inflation factor(VIF) are most popular. In this study, we give new interpretations of condition number and VIF in linear regression, using geometry on the explanatory space. In the same line, we derive natural measures of condition number and VIF for logistic regression. These computer intensive measures can be easily extended to evaluate multicollinearity in generalized linear models.

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공간분석을 이용한 지역별 비만율에 영향을 미치는 요인분석 (Analysing the Effects of Regional Factors on the Regional Variation of Obesity Rates Using the Geographically Weighted Regression)

  • 김다양;곽진미;서은원;이광수
    • 보건행정학회지
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    • 제26권4호
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    • pp.271-278
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    • 2016
  • Background: This study purposed to analyze the relationship between regional obesity rates and regional variables. Methods: Data was collected from the Korean Statistical Information Service (KOSIS) and Community Health Survey in 2012. The units of analysis were administrative districts such as city, county, and district. The dependent variable was the age-sex adjusted regional obesity rates. The independent variables were selected to represent four aspects of regions: health behaviour factor, psychological factor, socio-economic factor, and physical environment factor. Along with the traditional ordinary least square (OLS) regression analysis model, this study applied geographically weighted regression (GWR) analysis to calculate the regression coefficients for each region. Results: The OLS results showed that there were significant differences in regional obesity rates in high-risk drinking, walking, depression, and financial independence. The GWR results showed that the size of regression coefficients in independent variables was differed by regions. Conclusion: Our results can help in providing useful information for health policy makers. Regional characteristics should be considered when allocating health resources and developing health-related programs.

A Study on the Performances of Strategic Alliance in Liner Shipping

  • Kim, Hyun-Duk;Ahn, Ki-Myoung;Lee, Sung-Yhun
    • 한국항해항만학회지
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    • 제30권7호
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    • pp.579-583
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    • 2006
  • The purpose of this paper is to study a relationship between alliance success factors and performances of strategic alliance. In order to achieve the purpose of this research, factor analysis, reliability and validity and regression method are used. In conclusion, alliance success factors can be divided as mutual complementarity and information sharing factor, sharing and mutual agreement of vision and goal factor, performance management factor and culture and organization factor. According to regression results, all of four factors affects significantly dependent variables. Among them, mutual complementarity and information sharing mostly affects each dependent variable.

Imputation Using Factor Score Regression

  • Lee, Sang-Eun;Hwang, Hee-Jin;Shin, Key-Il
    • Communications for Statistical Applications and Methods
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    • 제16권2호
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    • pp.317-323
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    • 2009
  • Recently not even government polices but small town decisions are based on the survey data/information, so the most of government agencies/organizations demand various sample surveys in each fields for more detail information. However in conducting the sample survey, nonresponse problem rises very often and it becomes a major issue on judging the accuracy of survey. For that matters, one solution ran be using the administration data. However unfortunately most of administration data are restricted to the common users. The other solution can be the imputation. Therefore several method, of imputation are studied in various fields. In this study, in stead of the simple regression imputation method which is commonly used, factor score regression method is applied specially to the incomplete data which have the unit and item misting values in survey data. Here for simulation study, Consumer Expenditure Surveys in Korea are used.

Diagnostics of partial regression and partial residual plots

  • Lee, Jea-Young;Choi, Suk-Hwa
    • Journal of the Korean Data and Information Science Society
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    • 제11권1호
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    • pp.73-81
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    • 2000
  • The variance inflation factor can be expressed by the square of the ratio of t-statistics associated with slopes of partial regression and partial residual plots. Disagreement of two sides in the interpretation can be occurred, and we analyze it with some illustrations.

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Shrinkage Structure of Ridge Partial Least Squares Regression

  • Kim, Jong-Duk
    • Journal of the Korean Data and Information Science Society
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    • 제18권2호
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    • pp.327-344
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    • 2007
  • 다중공선성의 데이터에 사용되는 대표적인 편향회귀방법은 능형회귀(RR), 주성분회귀(PCR), 부분최소제곱회귀(PLS) 등이다. 이 회귀방법들은 계수베거 추정량의 놈(norm)이 모두 보통 최소제곱회귀(OLS)의 추정량의 놈보다 작아진다는 의미에서 축소회귀라 부른다. 새로운 회귀방법으로 RR과 PCR을 결합한 능형주성분회귀(RPCR)가 있고 RR과 PLS를 결합한 능형부분최소제곱회귀(RPLS)가 있으며 이들도 또한 축소회귀이다. 이들 추정량은 X'X의 고유벡터들의 선형결합으로 나타낼 수 있고 따라서 각 고유방향에서 OLS에 비해 얼마나 축소되는지를 연구할 수 있다. 본 논문에서는 먼저 이들 추정량을 일반적인 축소인자의 식으로 나타내고 이를 이용하여 MSE의 일반식을 구하였으며 PLS 추정량의 MSE 식도 구하였다. 그리고 RPLS의 축소인자 식을 두 가지 다른 형태로 유도하였다. RPLS의 경우도 이 축소인자 식을 MSE의 일반식에 대입하면 MSE 식이 바로 얻어진다. 그러나 PLS나 RPLS의 축소인자는 y의 복잡한 비선형이 되어 결정적이 아니므로 이들 추정량의 MSE는 근사적인 식이라 할 수 있다. 따라서 PLS나 RPLS를 평가하기 위해 이 MSE를 사용하는 것은 제한적이며, 경험적인 방법으로 이들 회귀의 수행성을 평가하는 것이 필요하다. 다중공선성의 대표적인 데이터인 근적외선 분광 데이터를 이용하여 이 유도된 회귀의 축소인자 값이 인자수에 따라 어떻게 변화하는지와 전체적인 축소 비율도 살펴보았다. 이들의 축소 형태를 잘 이해하면 회귀방법들의 예측력과 안정성을 파악하는데 많은 도움이 되리라 판단된다.

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동적요인모형에 기반한 한국의 GDP 성장률 예측 (Forecasting Korea's GDP growth rate based on the dynamic factor model)

  • 이경서;임예지
    • 응용통계연구
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    • 제37권2호
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    • pp.255-263
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    • 2024
  • GDP는 한 나라의 가계, 기업, 정부 등 모든 경제 주체가 일정 기간 동안 창출한 재화와 서비스의 시장 가치의 합을 나타낸다. GDP를 통하여 국가의 경제 규모를 파악할 수 있으며, 정부의 정책 방향에 영향을 미치는 대표적인 경제 지표이므로 이에 대한 연구가 다양하게 이루어지고 있다. 본 논문에서는 G20 국가들의 주요 거시경제 지표를 활용하여 dynamic factor model 기반의 GDP 성장률 예측 모델을 제시하였다. 추출된 factor를 다양한 회귀분석 방법론과 결합하여 그 결과들을 비교하였으며, 기존의 전통적인 시계열 예측방법인 ARIMA 모델, common component를 이용한 예측 등도 함께 비교하였다. COVID 이후 지표의 변동성이 큰 점을 고려하여 예측 시기를 COVID 전후로 나누었으며, 그 결과 factor에 대해 ridge regression과 lasso regression을 적용하여 예측한 경우 가장 좋은 성능을 나타내었다.

한국 제조업의 임금결정에 대한 연구 : 외환위기 전·후를 중심으로 (The Study of Korean Manufacturing Industry Wage : Principal Components Regression Analysis)

  • 오유진;박성준;김유섭
    • 노동경제논집
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    • 제28권1호
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    • pp.61-82
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    • 2005
  • 본 연구는 경제 위기 이후 한국 제조업의 산업간 임금격차가 경제위기 이전 그대로 유지되고 있는가의 여부와, 임금을 결정하는 메커니즘이 외환위기를 지나면서 어떻게 변화하였는가에 관하여 분석하였다. 분석에 사용된 자료는 1995년도와 1999년도 노동부의 "임금구조기본통계조사"이고, 주된 계량 기법으로는 요인분석(factor analysis)과 주성분 회귀분석(principal components regression)을 사용하였다. 분석 결과, 제조업의 산업간 임금격차는 더 벌어졌으며 임금결정의 주된 요인도 외환위기 이전에는 개인의 업무능력이었으나 위기 이후에는 사업체의 특성인 것으로 밝혀졌다.

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Prediction of curvature ductility factor for FRP strengthened RHSC beams using ANFIS and regression models

  • Komleh, H. Ebrahimpour;Maghsoudi, A.A.
    • Computers and Concrete
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    • 제16권3호
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    • pp.399-414
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    • 2015
  • Nowadays, fiber reinforced polymer (FRP) composites are widely used for rehabilitation, repair and strengthening of reinforced concrete (RC) structures. Also, recent advances in concrete technology have led to the production of high strength concrete, HSC. Such concrete due to its very high compression strength is less ductile; so in seismic areas, ductility is an important factor in design of HSC members (especially FRP strengthened members) under flexure. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and multiple regression analysis are used to predict the curvature ductility factor of FRP strengthened reinforced HSC (RHSC) beams. Also, the effects of concrete strength, steel reinforcement ratio and externally reinforcement (FRP) stiffness on the complete moment-curvature behavior and the curvature ductility factor of the FRP strengthened RHSC beams are evaluated using the analytical approach. Results indicate that the predictions of ANFIS and multiple regression models for the curvature ductility factor are accurate to within -0.22% and 1.87% error for practical applications respectively. Finally, the effects of height to wide ratio (h/b) of the cross section on the proposed models are investigated.