In this paper, we have considered the modeling and analyses of categorical data. We modeled binary data with categorical predictors, using logistic regression to develop a statistical method. We found that ANOVA-type analyses often performed unsatisfactory, even when using arcsine-square-root transformations. We concluded that such methods are not appropriate, especially in cases where the fractions were close to 0 or 1. The logistic transformation of fraction data could be a promising alternative, but it is not desirable in the statistical sense. The major purpose of this paper is to demonstrate that logistic regression with an ANOVA-model like parameterization aids our understanding and provides a somewhat different, but sound, statistical background. We examined a simple real-world example to show that we can efficiently test the significance of regression parameters, look for interactions, estimate confidence intervals, and calculate the difference between the mean values of the referent and experimental subgroups. This paper demonstrates that precise confidence interval estimates can be obtained using the proposed ANOVA-model like approach. The method discussed here can be extended to any type of fraction data analysis, particularly for experimental design.

PURPOSES : This study is to construct the regression models of drainage asphalt concrete specimens and to provide the appropriate coefficients of hydraulic conductivity prediction models. METHODS: In terms of easy calculation of the hydraulic conductivity from porosity of asphalt concrete pavement, the estimation model of hydraulic conductivity was proposed using regression analysis. 10 specimens of drainage asphalt concrete pavement were made for measurement of the hydraulic conductivity. Hydraulic conductivity model proposed in this study was calculated by empirical model based on porosity and the grain size. In this study, it shows the compared results from permeability measured test and empirical equation, and the suitability of proposed model, using regression analysis. RESULTS: As the result of the regression analysis, the hydraulic conductivity calculated from the proposal model was similar to that resulted from permeability measured test. Also result of RMSE (Root Mean Square Error) analysis, a proposed regression model is resulted in more accurate model. CONCLUSIONS: The proposed model can be used in case of estimating the hydraulic conductivity at drainage asphalt concrete pavements in fields.

An attempt is given to the problem of analyzing the two-way binary attribute data using the logistic regression model in order to find a sound statistical methodology. It is demonstrated that the analysis of variance (ANOVA) may not be good enough, especi

In this paper, an analysis of two-way binary attribute data is performed using the logistic regression model in order to find a sound statistical methodology. It is demonstrated that the ANOVA may not be enough, especially for the case that the proportion is very low or high. The logistic transformation of proportion data could be a help, but not sound in the statistical sense. The adoption of generalized least squares(GLS) method entails much to estimate the variance-covariance matrix. On the other hand, the logistic regression methodology provides sound statistical background in estimating model parameters and related confidence intervals. The efficiencies of estimates are ensured with a simulated data with a view to demonstrate the usefulness of the methodology.

모형저항시험 자료를 토대로 선형요소들에 대한 통계 회귀계수를 만들고, 이를 이용하여 새로운 어선의 기본설계 단계에서 유효마력을 추정할 수 있는 방법을 정립하였다. 이러한 방법을 Chine Line이 있는 소형 표준어선 4척과 Round Bottom형 표준어선 2척에 적용하여 유효마력을 추정하였으며, 이를 실험값과 비교하였다. 이 6척 어선의 경우 전속도 범위에서 추정치와 실험치가 잘 일치하고 있는 것과 근접한 것으로 나타났다. 그러므로 작성된 수치계산법은 어선의 유효마력추정에 매우 유용하게 쓰일 수 있음이 입증되었다. 따라서 제안된 방법을 어선설계에 적용하면, 직접적으로는 저항성능을 개선할 수 있을 뿐 아니라, 설계공수단축, 원가절감 등에 기여할 수 있을 것이다.