• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.141-151
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• 2010

Hybrid fuzzy regression analysis is used for integrating randomness and fuzziness into a regression model. Least squares support vector machine(LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate hybrid fuzzy linear and nonlinear regression models with crisp inputs and fuzzy output using weighted fuzzy arithmetic(WFA) and LS-SVM. LS-SVM allows us to perform fuzzy nonlinear regression analysis by constructing a fuzzy linear regression function in a high dimensional feature space. The proposed method is not computationally expensive since its solution is obtained from a simple linear equation system. In particular, this method is a very attractive approach to modeling nonlinear data, and is nonparametric method in the sense that we do not have to assume the underlying model function for fuzzy nonlinear regression model with crisp inputs and fuzzy output. Experimental results are then presented which indicate the performance of this method.

• Journal of the Korean Society of Clothing and Textiles
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• v.35 no.11
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• pp.1322-1332
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• 2011

This study investigates the influence of social desirability to questionnaire response and data analysis in order to identify the need for social desirability control in clothing consumer research. A questionnaire measuring social desirability, social face sensitivity, clothing shopping behavior, and demographic characteristics was developed. Responses of 234 respondents were analyzed using factor analysis, simple regression analysis, hierarchical regression analysis, descriptive analysis, and Cronbach's alpha analysis. The results were as follow. First, respondents were influenced by social desirability when they responded to items measuring other-conscious social face. Second, the result of regression analysis (that the independent variable was social formality) was less influenced by social desirability control because the influence of social desirability to social formality was insignificant. Conversely, the result of regression analysis (that the independent variable was other-conscious social face) was more influenced by social desirability control because the influence of social desirability to other-conscious social face was significant. This study is an initial study that notices the need for social desirability control in clothing consumer research.

• International Journal of Advanced Culture Technology
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• v.9 no.1
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• pp.143-151
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• 2021

This study was a cross-sectional descriptive research to analyze the effects of sub-factors of cognitive emotional control on happiness levels. The participants of the study were 201 men and women in their 20s, and data were collected online from January 1 to 15 collected data were, 2001 using structured cognitive control and happiness level questionnaires. The collected data were conducted Independent t-test, Pearson correlation analysis, simple regression analysis, multiple regression Analysis, hierarchical regression analysis using SPSS 18.0 statistic program. As a result, the study appeared that the level of happiness by gender does not differ, and cognitive emotional control affected 58.5%. The average of cognitive emotional control was higher for all men, but women were higher than men in criticized others. Also, acceptance was the sub-factor of emotional control that most affected the level of happiness (β=-.587, p<0.01). Based on the results of this study, it is suggested that a systematic program on subject of acceptance, a sub-factor of cognitive emotional control, should be developed to improve the level of happiness.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.217-228
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• 2018

Since the real estate reporting system was first introduced, about 2 million real estate transaction per year have been reported over the last 10 years with an increasing demand for real estate price estimates. This study looks at the applicability and superiority of the regression-kriging method to derive effective real transaction prices estimation on the location where information about real transaction is unavailable. Several issues on predicting the real estate price are discussed and illustrated using the real transaction reports of Jinju, Gyeongsangnam-do. Results have been compared with a simple regression model in terms of the mean absolute error and root square error. It turns out that the regression-kriging model provides a more effective estimation of land price compared to the simple regression model. The regression-kriging method adequately reflects the spatial structure of the term that is not explained by other characteristic variables.

• Computers and Concrete
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• v.16 no.4
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• pp.569-585
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• 2015

Various computational tools are available for modeling highly nonlinear structural engineering problems that lack a precise analytical theory or understanding of the phenomena involved. This paper adopts a fairly simple nonparametric adaptive regression algorithm known as multivariate adaptive regression splines (MARS) to model the nonlinear interactions between variables. The MARS method makes no specific assumptions about the underlying functional relationship between the input variables and the response. Details of MARS methodology and its associated procedures are introduced first, followed by a number of examples including three practical structural engineering problems. These examples indicate that accuracy of the MARS prediction approach. Additionally, MARS is able to assess the relative importance of the designed variables. As MARS explicitly defines the intervals for the input variables, the model enables engineers to have an insight and understanding of where significant changes in the data may occur. An example is also presented to demonstrate how the MARS developed model can be used to carry out structural reliability analysis.

• Korea Trade Review
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• v.44 no.2
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• pp.51-62
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• 2019

This study predicts the future world seaborne trade volume with econometrics methods using 23-year time series data provided by Clarksons. For this purpose, this study uses simple regression analysis, exponential smoothing method and seemingly unrelated regression model (SUR Model). This study is meaningful in that it predicts worldwide total seaborne trade volume and seaborne traffic in four major items (container, bulk, crude oil, and LNG) from 2019 to 2023 as there are few prior studies that predict future seaborne traffic using recent data. It is expected that more useful references can be provided to trade related workers if the analysis period was increased and additional variables could be included in future studies.

• Structural Engineering and Mechanics
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• v.45 no.6
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• pp.837-851
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• 2013

In construction industry, strength is a primary criterion in selecting a concrete for a particular application. The concrete used for construction gains strength over a long period of time after pouring the concrete. The characteristic strength of concrete is defined as the compressive strength of a sample that has been aged for 28 days. Neither waiting for 28 days for such a test would serve the rapidity of construction, nor would neglecting it serve the quality control process on concrete in large construction sites. Therefore, rapid and reliable prediction of the strength of concrete would be of great significance. On this backdrop, the method is proposed to establish a predictive relationship between properties and proportions of ingredients of concrete, compaction factor, weight of concrete cubes and strength of concrete whereby the strength of concrete can be predicted at early age. Multiple regression analysis was carried out for predicting the compressive strength of concrete containing Portland Pozolana cement using statistical analysis for the concrete data obtained from the experimental work done in this study. The multiple linear regression models yielded fairly good correlation coefficient for the prediction of compressive strength for 7, 28 and 40 days curing. The results indicate that the proposed regression models are effectively capable of evaluating the compressive strength of the concrete containing Portaland Pozolana Cement. The derived formulas are very simple, straightforward and provide an effective analysis tool accessible to practicing engineers.

• International Journal of Highway Engineering
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• v.14 no.4
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• pp.133-140
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• 2012

PURPOSES : Traffic situation of Seoul City is different each administrative district. because each administrative district population, average travel speed, etc are different. thus, regionally differentiated policy is necessary. METHODS : In this study, first, it is to implement the cluster analysis using the traffic factor of twenty-five administrative districts in Seoul, categorize it into the cluster and understand the properties. second, related factors of speed were derived. and method to increase the speed was investigated. we choose the eleven traffic factors such as the number of traffic accident cases, total length, speed, the number of cross section, the number of cross section per km, the rate of roads, registered cars, population attending office and school, population density, area. RESULTS : In the results, first, we could categorize the Seoul-City administrative district into three clusters. in order to find Factors associated with speed a simple regression analysis was performed. and the number of intersections per km is closely related to the speed. CONCLUSIONS : Through this study, transportation policies reflecting local traffic-related characteristics are required.

• Journal of Ocean Engineering and Technology
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• v.22 no.6
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• pp.95-99
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• 2008

The block assembly of ship consists of a certain type of heat processes such as cutting, bending welding residual stress relaxation and fairing. The residual deformation due to welding is inevitable at each assembly stage. The geometric inaccuracy caused by the welding deformation tends to preclude the introduction of automation and mechanization and needs the additional man-hours for the adjusting work at the following assembly stage. To overcome this problem, a distortion control method should be applied. For this purpose, it is necessary to develop an accurate prediction method which can explicitly account for the influence of various factors on the welding deformation. The validity of the prediction method must be also clarified through experiments. This paper proposes a simplified analysis method to predict the welding deformation of panel block structure. For this purpose, a simple prediction model for fillet welding deformations has been derived based on numerical and experimental results through the regression analysis. On the basis of these results, the simplified analysis method has been applied to some examples to show its validity.

• Geomechanics and Engineering
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• v.23 no.2
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• pp.115-125
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• 2020

This study aims to explore practical and useful equations for rapid evaluation of uniaxial compressive strength of concrete (UCS-C) during the preliminary design stage of aggregate selection. For this purpose, aggregates which were produced from eight different intact rocks were used in the production of concretes. Laboratory experiments involved the tests for uniaxial compressive strength (UCS-R), point load index (PLI-R), P wave velocity (UPV-R), apparent porosity (n-R), unit weight (UW-R) and aggregate impact value (AIV-R) of the rock samples. UCS-C, point load index (PLI-C) and P wave velocity (UPV-C) of concrete samples were also determined. Relationships between UCS-R-rock parameters and UCS-C-concrete parameters were developed by regression analyses. In the simple regression analyses, PLI-C, UPV-C, UCS-R, PLI-R, and UPV-R were found to be statistically significant independent variables to estimate the UCS-C. However, higher coefficients of determination (R²=0.97-1.0) were obtained by multiple regression analyses. The results of simple regression analysis were also compared to the limited number of previous studies. The strength conversion factor (k) values were found to be 14.3 and 14.7 for concrete and rock samples, respectively. It is concluded that the UCS-C can roughly be estimated from derived equations only for the specified rock types.