• Title/Summary/Keyword: Regression Analysis Model

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A Study on Productivity Factors of Chinese Container Terminals

  • Lu, Bo;Park, Nam-Kyu
    • Journal of Navigation and Port Research
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    • v.34 no.7
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    • pp.559-566
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    • 2010
  • The container port industry has been variously studied by many researchers, because the contemporary container transportation and container port industries play a pivotal role in globalization of the world economy. For container terminals, the productivity, affected by many factors, is an important target in measuring container terminal performance. Under this background, finding the critical factors affecting the productivity is necessary. Regression analysis can be used to identify which independent variables are related to the dependent variable, and explore the relationships of them. The aim of paper is to evaluate the factors affecting the productivity of Chinese major terminals by using a regression statistical analysis modeling approach, which is to establish the variable preprocessing model (VPM) and regression analysis model (RAM), by means of collecting the major Chinese container terminals data in the year of 2008.

On Logistic Regression Analysis Using Propensity Score Matching (성향점수매칭 방법을 사용한 로지스틱 회귀분석에 관한 연구)

  • Kim, So Youn;Baek, Jong Il
    • Journal of Applied Reliability
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    • v.16 no.4
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    • pp.323-330
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    • 2016
  • Purpose: Recently, propensity score matching method is used in a large number of research paper, nonetheless, there is no research using fitness test of before and after propensity score matching. Therefore, comparing fitness of before and after propensity score matching by logistic regression analysis using data from 'online survey of adolescent health' is the main significance of this research. Method: Data that has similar propensity in two groups is extracted by using propensity score matching then implement logistic regression analysis on before and after matching separately. Results: To test fitness of logistic regression analysis model, we use Model summary, -2Log Likelihood and Hosmer-Lomeshow methods. As a result, it is confirmed that the data after matching is more suitable for logistic regression analysis than data before matching. Conclusion: Therefore, better result which has appropriate fitness will be shown by using propensity score matching shows better result which has better fitness.

A Study on Demand Forecasting Model of Domestic Rare Metal Using VECM model (VECM모형을 이용한 국내 희유금속의 수요예측모형)

  • Kim, Hong-Min;Chung, Byung-Hee
    • Journal of the Korean Society for Quality Management
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    • v.36 no.4
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    • pp.93-101
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    • 2008
  • The rare metals, used for semiconductors, PDP-LCS and other specialized metal areas necessarily, has been playing a key role for the Korean economic development. Rare metals are influenced by exogenous variables, such as production quantity, price and supplied areas. Nowadays the supply base of rare metals is threatened by the sudden increase in price. For the stable supply of rare metals, a rational demand outlook is needed. In this study, focusing on the domestic demand for chromium, the uncertainty and probability materializing from demand and price is analyzed, further, a demand forecast model, which takes into account various exogenous variables, is suggested, differing from the previously static model. Also, through the OOS(out-of-sampling) method, comparing to the preexistence ARIMA model, ARMAX model, multiple regression analysis model and ECM(Error Correction Mode) model, we will verify the superiority of suggested model in this study.

An Economic Analysis of the Determinants of Studio Apartment Prices in Seoul

  • Jeong, Seung-Young;Son, Jin-A
    • Journal of Distribution Science
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    • v.12 no.9
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    • pp.47-52
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    • 2014
  • Purpose - There has been little research on the variables influencing studio apartment values. This study aims to identify variables affecting the value of studio apartments in Seoul by empirically examining the interaction between sale prices and characteristics studio apartment characteristics. Research design, data, and methodology - We have analyzed data pertaining to 142 studio apartments in September 2010. A regression analysis model is constructed to test the significance of the variables in relation to the studio apartment sale prices per m2 in Seoul. Results - The age of the building is comparatively more significant than land use as the explanatory variable. Land price is the key variable affecting studio apartment sale prices and investors are willing to pay high implicit sale prices for locations that are associated with high land prices. Conclusions - The age of buildings explains a significant portion of the variability of the sale prices of studio apartment. Higher land prices result in higher sale prices for studio apartments. The older the buildings, the lower the sale prices of the studio apartments.

A Study on the Estimation Method of Concrete Compressive Strength Based on Machine Learning Algorithm Considering Mixture Factor (배합 인자를 고려한 Machine Learning Algorithm 기반 콘크리트 압축강도 추정 기법에 관한 연구)

  • Lee, Seung-Jun;Lee, Han-Seung
    • Proceedings of the Korean Institute of Building Construction Conference
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    • pp.152-153
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    • 2017
  • In the construction site, it is necessary to estimate the compressive strength of concrete in order to adjust the demolding time of the form, and establish and adjust the construction schedule. The compressive strength of concrete is determined by various influencing factors. However, the conventional method for estimating the compressive strength of concrete has been suggested by considering only 1 to 3 specific influential factors as variables. In this study, six influential factors (Water, Cement, Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at three conferences in order to know the various correlations among data and the tendency of data. After using algorithm of various methods of machine learning techniques, we selected the most suitable regression analysis model for estimating the compressive strength.

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Development of the Linear Regression Analysis Model to Estimate the Shear Strength of Soils (흙의 전단강도 산정을 위한 선형회귀분석모델 개발)

  • Lee, Moon-Se;Ryu, Je-Cheon;Kim, Kyeong-Su
    • The Journal of Engineering Geology
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    • v.19 no.2
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    • pp.177-189
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    • 2009
  • The shear strength has been managed as an important factor in soil mechanics. The shear strength estimation model was developed to evaluate the shear strength using only a few soil properties by the linear regression analysis model which is one of the statistical methods. The shear strength is divided into two part; one is the internal friction angle (${\phi}$) and the other is the cohesion (c). Therefore, some valid soil factors among the results of soil tests are selected through the correlation analysis using SPSS and then the model are formulated by the linear regression analysis based on the relationship between factors. Also, the developed model is compared with the result of direct shear test to prove the rationality of model. As the results of analysis about relationship between soil properties and shear strength, the internal friction angle is highly influenced by the void ratio and the dry unit weight and the cohesion is mainly influenced by the void ratio, the dry unit weight and the plastic index. Meanwhile, the shear strength estimated by the developed model is similar with that of the direct shear test. Therefore, the developed model may be used to estimate the shear strength of soils in the same condition of study area.

The Study for Damage Effect Factors of Heavy Snowfall Disasters : Focused on Heavy Snowfall Disasters during the Period of 2005 to 2014 (대설 재난의 피해액 결정요인에 관한 연구: 2005~2014년 대설재난을 중심으로)

  • Kim, Geunyoung;Joo, Hyuntae;Kim, HeeJae
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.19 no.2
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    • pp.125-136
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    • 2018
  • Heavy snowfall disasters are the third most serious natural disasters, after typhoon and heavy rainfall disasters, in terms of economic disaster damage in South Korea. The average annual economic damage of heavy snowfall disasters was approximately eighty-eight billion won during the period of 2005-2014. In spite of significant economic damage, there have been few economic studies regarding heavy snowfall disasters in South Korea. The objective of this research is to identify the association between economic damage of heavy snowfall disasters and damage effect factors of snowfall amounts, snowfall days, population densities, and non-urban area ratios using a regression analysis model. Economic damage data sets of heavy snowfall disasters during the period of 2005-2014 were obtained from the Natural Disaster Yearbook published by the Ministry of Public Safety and Security. Weather-related data sets, such as snowfall amounts and snowfall days were collected from the Korea Meteorological Administration. Demographic and urban data sets, including population densities and non-urban area ratios, were provided by the Local Government Yearbook. Outcomes of this study can assist with heavy snowfall disaster management policies of South Korea.

Prediction of Effective Horsepower for G/T 4 ton Class Coast Fishing Boat Using Statistical Analysis (통계해석에 의한 G/T 4톤급 연안어선의 유효마력 추정)

  • Park, Chung-Hwan;Shim, Sang-Mog;Jo, Hyo-Jae
    • Journal of Ocean Engineering and Technology
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    • v.23 no.6
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    • pp.71-76
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    • 2009
  • This paper describes a statistical analysis method for predicting a coast fishing boat's effective horsepower. The EHP estimation method for small coast fishing boats was developed, based on a statistical regression analysis of model test results in a circulating water channel. The statistical regression formula of a fishing boat's effective horsepower is determined from the regression analysis of the resistance test results for 15 actual coast fishing boats. This method was applied to the effective horsepower prediction of a G/T 4 ton class coast fishing boat. From the estimation of the effective horsepower using this regression formula and the experimental model test of the G/T 4 ton class coast fishing boat, the estimation accuracy was verified under 10 percent of the design speed. However, the effective horsepower prediction method for coast fishing boats using the regression formula will be used at the initial design and hull-form development stage.

A Study on the Flank Wear of Carbide Tool in Machining SUS304 (SUS304 절삭시 Carbide 공구의 Crater 마모에 관한 연구)

  • Jeong, Jin-Yong;O, Seok-Hyeong;Kim, Jong-Taek;Seo, Nam-Seop
    • Journal of the Korean Society for Precision Engineering
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    • v.8 no.3
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    • pp.44-54
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    • 1991
  • A Study was made on falnk wear in carbide tools in turning SUS304 steel. When an austenitic stainless steel (SUS304 steel) is cut with the tool, saw-toothed chip are produced. It is found that machining SUS304 steel would make a tool worn fast. For increasing productivity, tool wear has to be predicted and controlled. An amended cutting geometry consisting of a negative rake angle ($-6^{\circ}$ ) and a high clearance angle ($-17^{\circ}$ ) is proposed for decreasing carbide tool wear (flank) in the machining of SUS304 steel. The amended cutting geometry is found to make the flank wear lower than a general cutting geometry (rake angle $6^{\circ}$ , clearance angle $5^{\circ}$). The effects of the three cutting variables (cutting speed, feed, tool radius) on the flank wear analyzed by fiting a simple first-order model containing interaction terms to each flank wear parameter by means of regression analysis and the predicted from first-order regression analysis model equation of flank wear.

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

  • Kim, Da Yang;Kwak, Jin-Mi;Seo, Eun-Won;Lee, Kwang-Soo
    • Health Policy and Management
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    • v.26 no.4
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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.