• Title/Summary/Keyword: Explanatory model

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Development of an Explanatory Model for Decision of Fashion Style and Its Diffusion Process Based on Ambivalence of Pursuit Values (유행 스타일의 결정과 확산에 대한 설명모형 연구 -추구가치의 양면성을 중심으로-)

  • 김선숙;이은영
    • Journal of the Korean Society of Clothing and Textiles
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    • v.19 no.4
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    • pp.637-650
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    • 1995
  • The purpose of the study was to develop a model to explain how a fashion style is determined within a society and how the style diffuses. The research was carried out in two stages, theoretical study followed by empirical study. In the theoretical study, explanatory model about decision of fashion style and diffusion was developed and then fashion diffusion theories and fashion phenomenon of postholder society were explained by the model developed. The theoretical framework of the explanatory model was constructed in that fashion changes by ambivalence of pursuit values within an individual as well as within a society. The empirical study was carried out to validate the model by looking into fashion phenomenon in the postmodern society A questionnaire was developed including style image, pursuit value, preference style and administered to 19 to 30 year-old women living in Seoul area. Frequency distribution, discriminant analysis, one-way ANOVA. were used for the statistical analysis. As pursuit values differed in each style preference stoup, and pursuit value coincided with image of preference style it was confirmed that clothing selection behavior was determined by pursuit value. In a postmodern society where variety of values are pursued, appearance of various products and preference of all styles altogether considered, it could assume that subcultural collective selection phenomenon appeared.

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Analysis of statistical models on temperature at the Seosan city in Korea (충청남도 서산시 기온의 통계적 모형 연구)

  • Lee, Hoonja
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.6
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    • pp.1293-1300
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    • 2014
  • The temperature data influences on various policies of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly and seasonal temperature data at the northern part of the Chungcheong Namdo, Seosan monitoring site in Korea. In the ARE model, five meteorological variables, four greenhouse gas variables and five pollution variables are used as the explanatory variables for the temperature data set. The five meteorological variables are wind speed, rainfall, radiation, amount of cloud, and relative humidity. The four greenhouse gas variables are carbon dioxide ($CO_2$), methane ($CH_4$), nitrous oxide ($N_2O$), and chlorofluorocarbon ($CFC_{11}$). And the five air pollution explanatory variables are particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), ozone ($O_3$), and carbon monoxide (CO). The result showed that the monthly ARE model explained about 39-63% for describing the temperature. However, the ARE model will be expected better when we add the more explanatory variables in the model.

Construction of Delay Predictive Models on Freeway Ramp Junctions (고속도로 진출입램프 접속부상의 지체예측모형 구축에 관한 연구)

  • 김정훈;김태곤
    • Journal of Korean Port Research
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    • v.14 no.2
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    • pp.175-185
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    • 2000
  • Today freeway is experiencing a severe congestion with incoming or outgoing traffic through freeway ramps during the peak periods. Thus, the purpose of this study is to identify the traffic characteristics, analyze the relationships between the traffic characteristics and finally construct the delay predictive models on the rap junctions of freeway with 70mph speed limit. From the traffic analyses, and model construction and verification for delay prediction on the ramp junctions of freeway, the following results were obtained : ⅰ) Traffic flow showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period. ⅱ) The occupancy also showed a big difference depending on the time periods, and the downstream occupancy(Od) was especially shown to have a higher explanatory power for the delay predictive model construction on the ramp junctions of freeway. ⅲ) The delay-occupancy curve showed a remarkable shift based on the occupancies observed : O$\_$d/〈9% and O$\_$d/$\geq$9%. Especially, volume and occupancy were shown to be highly explanatory for delay prediction on the ramp junctions of freeway under O$\_$d/$\geq$9%, but lowly for delay prediction on the ramp junctions of freeway under O$\_$d/〈9%. Rather, the driver characteristics or transportation conditions around the freeway were thought to be a little higher explanatory for the delay prediction under O$\_$d/〈9%. ⅳ) Integrated delay predictive models showed a higher explanatory power in the morning peak period, but a lower explanatory power in the non-peak periods.

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Construction of Delay Predictine Models on Freeway Ramp Junctions with 70mph Speed Limit (70mph 제한속도를 갖는 고속도로 진출입램프 접속부상의 지체예측모형 구축에 관한 연구)

  • 김정훈;김태곤
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 1999.10a
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    • pp.131-140
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    • 1999
  • Today freeway is experiencing a severe congestion with incoming or outgoing traffic through freeway ramps during the peak periods. Thus, the objectives of this study is to identify the traffic characteristics, analyze the relationships between the traffic characteristics and finally construct the delay predictive models on the ramp junctions of freeway with 70mph speed limit. From the traffic analyses, and model constructions and verifications for delay prediction on the ramp junctions of freeway, the following results were obtained: ⅰ) Traffic flow showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy also showed a big difference depending on the time periods, and the downstream occupancy(Od) was especially shown to have a higher explanatory power for the delay predictive model construction on the ramp junction of freeway. ⅲ) The speed-occupancy curve showed a remarkable shift based on the occupancies observed ; Od < 9% and Od$\geq$9%. Especially, volume and occupancy were shown to be highly explanatory for delay prediction on the ramp junctions of freeway under Od$\geq$9%, but lowly for delay predicion on the ramp junctions of freeway under Od<9%. Rather, the driver characteristics or transportation conditions around the freeway were through to be a little higher explanatory for the delay perdiction under Od<9%. ⅳ) Integrated delay predictive models showed a higher explanatory power in the morning peak period, but a lower explanatory power in the non-peak periods.

A Comparison on Confidence Intervals for P(X>Y) with Explanatory Variables

  • Lee, In-Suk;Cho, Jang-Sik
    • Journal of Korean Society for Quality Management
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    • v.25 no.1
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    • pp.193-203
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    • 1997
  • In this paper, we obtain some a, pp.oximate confidence intervals for the reliability of the stress-strength model when the stress and strength each depend on some explanatory variables, respectively. Also we compare the confidence intervals via Monte Carlo simulation.

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An Adaptive Framework for Forecasting Demand and Technological Substitution

  • Kang, Byung-Ryong;Han, Chi-Moon;Yim, Chu-Hwan
    • ETRI Journal
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    • v.18 no.2
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    • pp.87-106
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    • 1996
  • This paper proposes a new model as a framework for forecasting demand and technological substitution, which can accommodate different patterns of technological change. This model, which we named, "Adaptive Diffusion Model", is formalized from a conceptual framework that incorporates several underlying factors determining the market demand for technological products. The formulation of this model is given in terms of a period analysis to improve its explanatory power for dynamic processes in the real world, and is described as a continuous form which approximates a discrete derivation of the model. In order to illustrate the applicability and generality of this model, time-series data of the diffusion rates for some typical products in electronics and telecommunications market have been empirically tested. The results show that the model has higher explanatory power than any other existing model for all the products tested in our study. It has been found that this model can provide a framework which is sufficiently robust in forecasting demand and innovation diffusion for various technological products.

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Predicting Gross Box Office Revenue for Domestic Films

  • Song, Jongwoo;Han, Suji
    • Communications for Statistical Applications and Methods
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    • v.20 no.4
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    • pp.301-309
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    • 2013
  • This paper predicts gross box office revenue for domestic films using the Korean film data from 2008-2011. We use three regression methods, Linear Regression, Random Forest and Gradient Boosting to predict the gross box office revenue. We only consider domestic films with a revenue size of at least KRW 500 million; relevant explanatory variables are chosen by data visualization and variable selection techniques. The key idea of analyzing this data is to construct the meaningful explanatory variables from the data sources available to the public. Some variables must be categorized to conduct more effective analysis and clustering methods are applied to achieve this task. We choose the best model based on performance in the test set and important explanatory variables are discussed.

A Study on the TAM (Technology Acceptance Model) in Involuntary IT Usage Environment (비자발적 IT 사용 환경에서의 기술 수용모델(TAM)에 관한 연구)

  • Moon, Hyung-Do;Kim, Jun-Woo
    • Journal of Digital Convergence
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    • v.7 no.3
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    • pp.13-24
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    • 2009
  • Technology Acceptance Model (TAM) has been a basis model for testing technology use. Post researches of TAM have been conducted with the updating the TAM by adding new independent variables in order to increase the explanatory power of the model. However, the problem is that different independent variables have to be required to keep the explanatory power whenever adopting particular technology. This might reduce the generality of the research model. Thus in order to increase the generality of the model, this study reviewed the previous researches and collected the independent variables used, and regrouped them into three basic independent constructs. New research model was designed with three basic independent constructs with three constructs selected for the involuntary information technology usage environment. Finally, this study concluded that new technology acceptance model could be used to explain the use of new technology without any adding new particular independent variables.

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An educational tool for regression models with dummy variables using Excel VBA (엑셀 VBA을 이용한 가변수 회귀모형 교육도구 개발)

  • Choi, Hyun Seok;Park, Cheolyong
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.3
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    • pp.593-601
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    • 2013
  • We often need to include categorial variables as explanatory variables in regression models. The categorial variables in regression models can be quantified through dummy variables. In this study, we provide an education tool using Excel VBA for displaying regression lines along with test results for regression models with a continuous explanatory variable and one or two categorical explanatory variables. The regression lines with test results are provided step by step for the model(s) with interaction(s), the model(s) without interaction(s) but with dummy variables, and the model without dummy variable(s). With this tool, we can easily understand the meaning of dummy variables and interaction effect through graphics and further decide which model is more suited to the data on hand.

Analysis of Time Series Models for Ozone Concentrations at the Uijeongbu City in Korea

  • Lee, Hoon-Ja
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.4
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    • pp.1153-1164
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    • 2008
  • The ozone data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model have been considered for analyzing the ozone data at the northern part of the Gyeonggi-Do, Uijeongbu monitoring site in Korea. The result showed that both overall and monthly ARE models are suited for describing the ozone concentration. In the ARE model, seven meteorological variables and four pollution variables are used as the as the explanatory variables for the ozone data set. The seven meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, dew point temperature, steam pressure, and amount of cloud. The four air pollution explanatory variables are Sulfur dioxide(SO2), Nitrogen dioxide(NO2), Cobalt(CO), and Promethium 10(PM10). Also, the high level ozone data (over 80ppb) have been analyzed four ARE models, General ARE, HL ARE, PM10 add ARE, Temperature add ARE model. The result shows that the General ARE, HL ARE, and PM10 add ARE models are suited for describing the high level of ozone data.

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