• Title/Summary/Keyword: Regression Analysis Model

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A Study on Forecast of Oyster Production using Time Series Models (시계열모형을 이용한 굴 생산량 예측 가능성에 관한 연구)

  • Nam, Jong-Oh;Noh, Seung-Guk
    • Ocean and Polar Research
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    • v.34 no.2
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    • pp.185-195
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    • 2012
  • This paper focused on forecasting a short-term production of oysters, which have been farmed in Korea, with distinct periodicity of production by year, and different production level by month. To forecast a short-term oyster production, this paper uses monthly data (260 observations) from January 1990 to August 2011, and also adopts several econometrics methods, such as Multiple Regression Analysis Model (MRAM), Seasonal Autoregressive Integrated Moving Average (SARIMA) Model, and Vector Error Correction Model (VECM). As a result, first, the amount of short-term oyster production forecasted by the multiple regression analysis model was 1,337 ton with prediction error of 246 ton. Secondly, the amount of oyster production of the SARIMA I and II models was forecasted as 12,423 ton and 12,442 ton with prediction error of 11,404 ton and 11,423 ton, respectively. Thirdly, the amount of oyster production based on the VECM was estimated as 10,425 ton with prediction errors of 9,406 ton. In conclusion, based on Theil inequality coefficient criterion, short-term prediction of oyster by the VECM exhibited a better fit than ones by the SARIMA I and II models and Multiple Regression Analysis Model.

Development of Accident Analysis Model in Car to Pedestrian Accident (차 대 보행자 충돌 시 사고해석 모델 개발)

  • Kang, D.M.;Ahn, S.M.
    • Journal of Power System Engineering
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    • v.13 no.5
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    • pp.76-81
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    • 2009
  • The fatalities of pedestrian account for about 21.2% of all fatalities at 2007 year in Korea. To reconstruct exactly the accident, it is important to calculate the throw distance of pedestrian in car to pedestrian accident. The frontal shape of SUV vehicle is dissimilar to passenger car and bus, so the trajectory and throw distance of pedestrian by SUV vehicle is not the same of passenger car and bus. The influencing on it can be classified into the factors of vehicle and pedestrian, and road factor. It was analyzed by PC-CRASH for simulation, and SPSS s/w was used for regression analysis. From the simulation results, the maximum impact energy of multi-body of pedestrian was occurred to that of torso body at the same time. And the throw distance increased with the increasing of impact velocity, and decreased with the increasing of impact offset. Also it decreased with the increasing of velocity of pedestrian at accident, and the throw distance of wet road was longer than that of dry road. Finally, the regression analysis model of SUV(Nissan Pathfinder type)vehicle in car to pedestrian accident was as follows; $$disti_i=-0.87-0.11offseti_i+0.69speed_i-4.27height_i+0.004walk_i+0.63wet_i+{\epsilon}_i$$.

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A Study on increasing the fitness of forecasts using Dynamic Model (동적 모형에 의한 예측치의 정도 향상에 관한 연구)

  • 윤석환;윤상원;신용백
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.19 no.40
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    • pp.1-14
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    • 1996
  • We develop a dynamic demand forecasting model compared to regression analysis model and AutoRegressive Integrated Moving Average(ARIMA) model. The dynamic model can apply to the current dynamic data to forecasts through introducing state equation. A multiple regression model and ARIMA model using given data are designed via the model analysis. The forecasting fitness evaluation between the designed models and the dynamic model is compared with the criterion of sum of squared error.

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University Students' Economic Distress and Coping Behavior in Meal Management (대학생의 경제적 불안과 식생활 대처행동)

  • 서정희;홍순명
    • Journal of the Korean Home Economics Association
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    • v.38 no.1
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    • pp.39-49
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    • 2000
  • This research investigated the effect of socio-economic variables and economic distress variables on the university students' coping behavior in meal management. The data used in this research included 544 university students in Ulsan Areas. The independent explanatory power of socio-economic variables was larger than economic distress variables. But the explanatory power was increased in the regression analysis model that was included both the socio-economic variables and the economic distress variables. The influencing variables that effected the level of coping behavior in meal management were the amount of discretionary expenditure, gender, status of housing, employment distress and income distress.

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A Study on Accident Prediction Models for Chemical Accidents Using the Logistic Regression Analysis Model (로지스틱회귀분석 모델을 활용한 화학사고 사상사고 예측모형 개발 연구)

  • Lee, Tae-Hyung;Park, Choon-Hwa;Park, Hyo-Hyeon;Kwak, Dae-Hoon
    • Fire Science and Engineering
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    • v.33 no.6
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    • pp.72-79
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    • 2019
  • Through this study, we developed a model for predicting chemical accidents lead to casualties. The model was derived from the logistic regression analysis model and applied to the variables affecting the accident. The accident data used in the model was analyzed by studying the statistics of past chemical accidents, and applying independent variables that were statistically significant through data analysis, such as the type of accident, cause, place of occurrence, status of casualties, and type of chemical accident that caused the casualties. A significance of p < 0.05 was applied. The model developed in this study is meaningful for the prevention of casualties caused by chemical accidents and the establishment of safety systems in the workplace. The analysis using the model found that the most influential factor in the occurrence of casualty in accidents was chemical explosions. Therefore, there is an urgent need to prepare countermeasures to prevent chemical accidents, specifically explosions, from occurring in the workplace.

The Estimation of Collision Speed at the Intersection using Simulation (시뮬레이션을 통한 교차로 충돌 속도 추정)

  • Han, Chang-Pyoung;Cheon, Jeong-Hwan;Choi, Hong Ju
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.22 no.2
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    • pp.514-521
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    • 2021
  • When calculating an intersection collision speed using a formula, it is very difficult to grasp the degree of deceleration of a vehicle after the collision unless there is road surface trace in the entire section where each vehicle moved from the point of collision to their final positions after the collision. A vehicle's motion trajectory shows an irregular curve after a collision due to the effects of inertia based on the driving characteristics of the vehicle, the eccentric force according to the collision site, and the collision speed. Therefore, it is very important to set the appropriate departure angle after a collision for accurate collision speed analysis. In this study, based on experimental collision data using a computer simulation (PC-Crash), the correlation between an appropriate vehicle departure angle and the post-collision speed was analyzed, and then, a regression analysis model was derived. Through this, we propose a method to calculate collision speed by applying only the vehicle departure angle in some types of collisions for traffic accidents at intersections.

Application of Regression Analysis Model to TOC Concentration Estimation - Osu Stream Watershed - (회귀분석에 의한 TOC 농도 추정 - 오수천 유역을 대상으로 -)

  • Park, Jinhwan;Moon, Myungjin;Han, Sungwook;Lee, Hyungjin;Jung, Soojung;Hwang, Kyungsup;Kim, Kapsoon
    • Journal of Environmental Impact Assessment
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    • v.23 no.3
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    • pp.187-196
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    • 2014
  • The objective of this study is to evaluate and analyze Osu stream watershed water environment system. The data were collected from January 2009 to December 2011 including water temperature, pH, DO, EC, BOD, COD, TOC, SS, T-N, T-P and discharge. The data were used for principle component analysis and factor analysis. The results are as followes. The primary factors obtained from both the principal component analysis and the factor analysis were BOD, COD, TOC, SS and T-P. Once principal component analysis and factor analysis have been performed with the collected data and then the results will be applied to both simple regression model and multiple regression model. The regression model was developed into case 1 using concentrations of water quality parameters and case 2 using delivery loads. The value of the coefficient of determination on case 1 fell between 0.629 and 0.866; this was lower than case 2 value which fell between 0.946 and 0.998. Therefore, case 2 model would be a reliable choice.The coefficient of determination between the estimated figure using data which was developed to the regression model in 2012 and the actual measurement value was over 0.6, overall. It can be safely deduced that the correlation value between the two findings was high. The same model can be applied to get TOC concentrations in future.

The audit method of cooling energy performance in office building using the Simple Linear Regression Analysis Model

  • Park, Jin-Young;Kim, Seo-Hoon;Jang, Cheol-Young;Kim, Jong-Hun;Lee, Seung-Bok
    • KIEAE Journal
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    • v.15 no.5
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    • pp.13-20
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    • 2015
  • Purpose: In order to upgrade the energy performance of existing building, energy audit stage should be implemented first because it is useful method to find where the problems occur and know how much time and cost consumption for retrofit. In overseas researches, three levels of audit is proposed whereas there are no standards for audit in Korea. Besides, most studies use dynamic simulation in detail like audit level 3 even though the level 2 can save time and cost than level 3. Thus, this paper focused on audit level 2 and proposed the audit method with the simple linear regression analysis model. Method: Two parameters were considered for the simple regression analysis, which were the monthly electric use and the mean outdoor temperature data. The former is a dependent variable and the latter is a independent variable, and the building's energy performance profile was estimated from the regression analysis method. In this analysis, we found the abnormal point in cooling season and the more detailed analysis were conducted about the three heat source equipments. Result: Comparing with real and predicted models, the total consumption of predicted model was higher than real value as 23,608 kWh but it was the results that was reflected the compulsory control in 2013. Consequently, it was analyzed that the revised model could save the cooling energy as well as reduce peak electric use than before.

Analysis Model Evaluation based on IoT Data and Machine Learning Algorithm for Prediction of Acer Mono Sap Liquid Water

  • Lee, Han Sung;Jung, Se Hoon
    • Journal of Korea Multimedia Society
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    • v.23 no.10
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    • pp.1286-1295
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    • 2020
  • It has been increasingly difficult to predict the amounts of Acer mono sap to be collected due to droughts and cold waves caused by recent climate changes with few studies conducted on the prediction of its collection volume. This study thus set out to propose a Big Data prediction system based on meteorological information for the collection of Acer mono sap. The proposed system would analyze collected data and provide managers with a statistical chart of prediction values regarding climate factors to affect the amounts of Acer mono sap to be collected, thus enabling efficient work. It was designed based on Hadoop for data collection, treatment and analysis. The study also analyzed and proposed an optimal prediction model for climate conditions to influence the volume of Acer mono sap to be collected by applying a multiple regression analysis model based on Hadoop and Mahout.

Profitability determinants of hospitals (병원의 수익성 관련 요인)

  • 이윤석;유승흠
    • Health Policy and Management
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    • v.13 no.3
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    • pp.129-147
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    • 2003
  • This study is to grasp a trend of profitability classified by characteristics of hospitals and to analyze related factors. Subjects are 145 hospitals which have gotten the standardization audit by Korean Hospital Association during 1998-200l. Profitability was measured in the aspect of operation profit rate with operating margin to gross revenue as proxy variables. Independent variables were classified by general factors (ownership, number of beds, period of establishment, competition), financial factors (liabilities to total assets, current ratio, fixed ratio, total asset turnover, inventories turnover), and factors related to patient treatment (average length of stay, bed occupancy rate, new outpatient ratio, admission ratio of outpatients, number of patients per specialist, personnel costs per adjusted inpatient, administrative costs per adjusted inpatient). Hierarchical multiple regression analysis model was used in this study. As a result of hierarchical multiple regression analyzation of operating margin to gross revenue, adjustive $R^2$ of general factors was relatively more powerful. The factors had significant effect on operating margin to gross revenue were ownership(+), number of beds(+), competition(+), current ratio(+), fixed ratio(+), total asset turnover(+), personnel costs per adjusted inpatient(-).