• 사상체질의학회지
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• 제28권1호
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• pp.51-56
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• 2016

Objectives This study was to identify the types of errors in the statistical analysis and trends of previous reported papers that used various statistical techniques.Methods We have selected 118 original articles for statistical review from the OASIS(http://oasis.kiom.re.kr) and the Pubmed(http://www.pubmed.gov) in the field of Sasang constitutional medicine. Published year was restricted from 2011 to 2015.Results 1. The ANOVA(25.72%) was the statistic of choice overall, followed by the chi-square test(21.74%), regression analysis(14.13%), t-test(11.59%), and etc. 2. By examining the errors of the statistical methods, there were 42(59.2%) thesis with errors among 71 thesis using ANOVA, 19(31.7%) thesis among 60 thesis using chi-square test, and 35(89.7%) over 39 thesis using regression analysis.Conclusions To improve the quality of Sasang Constitution, the participation of statisticians in research design will reduce the significant errors in statistical interpretation of the results.

• 한국안전학회지
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• 제17권1호
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• pp.94-98
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• 2002

This study aimed to search for the fundamental accident causes using a categorical analysis, a kind of statistical methods. As the analysis methods, correlation analysis, independence test and logistic regression analysis were used. And the SPSS package, a general-purpose mathematical library, was used to obtain statistical characteristics. As the result of this study, the accident causes associated with factor of 'lost working days' were factors such as 'employed periods', 'sex', 'type of accident', 'month'. In case of applying independence test method, the most important cause was the factor of 'month'. In case that logistic regression analysis method was applied, the cause contributed to the increase structure'. 'less than 6 month'. On the basis of these results, the plan for accident prevention and the proper investment for accident prevention expenditure could be carried out in each workshop.

• 품질경영학회지
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• 제47권4호
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• pp.687-700
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• 2019

Purpose: The purpose of this study is to investigate the interaction effects between price determinants of artworks. We expand the methodology in art market by applying machine learning techniques to estimate the price of artworks and compare linear regression and machine learning in terms of prediction accuracy. Methods: Moderated regression analysis was performed to verify the interaction effects of artistic characteristics on price. The moderating effects were studied by confirming the significance level of the interaction terms of the derived regression equation. In order to derive price estimation model, we use multiple linear regression analysis, which is a parametric statistical technique, and k-nearest neighbor (kNN) regression, which is a nonparametric statistical technique in machine learning methods. Results: Mostly, the influences of the price determinants of art are different according to the auction types and the artist 's reputation. However, the auction type did not control the influence of the genre of the work on the price. As a result of the analysis, the kNN regression was superior to the linear regression analysis based on the prediction accuracy. Conclusion: It provides a theoretical basis for the complexity that exists between pricing determinant factors of artworks. In addition, the nonparametric models and machine learning techniques as well as existing parameter models are implemented to estimate the artworks' price.

• 품질경영학회지
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• 제18권1호
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• pp.84-95
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• 1990

The objective of this study is to understand the effect of measurement-related investment. That is, this study aims at verifying the correlation between the measurement-related investment and inferiority rate of products by statistical analysis. The samples of this study are 376 industrial companies in Korea, and the research data was analysed on inferiority state of industrial companies with a measurement standard laboratory. The analysis was made by the elementary statistics, the correlation analysis and the regression analysis. The results are summarized as follows : First, the inferioriy rate of the industrial companies with a measurement standard laboratory was relatively lower than that of the other companies without the laboratory by statistical significance. Second, the increment on measurement-related investment had a negative correlation with the increment of inferiority rate, and the increase of measurement-related investment showed decrease of the inferiority rate by regression analysis.

• 환경영향평가
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• 제4권2호
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• pp.93-103
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• 1995

This study was carried out to suggest the best statistical test to analyze the trend in monthly water quality data. Traditional parametric tests such as t-test and regression analysis are based on the assumption that the underlying population has a normal distribution and regression analysis additionally assumes that residual errors are independent. Analyzing 9-years monthly COD data collected at Paldang in Han River, the underlying population was found to be neither normal nor independent. Therefore parametric tests are invalid for trend detection. Four Kinds of nonparametric statistical tests, such as Run Test, Daniel test, Mann-Kendall test, and Time Series Residual Analysis were applied to analyze the trend in the COD data, Daniel test and Mann-Kendall test indicated upward trend in COD data. The best nonparametric test was suggested to be Daniel test, which is simple in computation and easy to understand the intuitive meaning.

• Journal of the Korean Statistical Society
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• 제26권4호
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• pp.493-505
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• 1997

The analysis of binary data appears to many areas such as statistics, biometrics and econometrics. In many cases, data are often collected in which some observations are incomplete. Assume that the missing covariates are missing at random and the responses are completely observed. A method to Bayesian analysis of the binary regression model with incomplete data is presented. In particular, the desired marginal posterior moments of regression parameter are obtained using Meterpolis algorithm (Metropolis et al. 1953) within Gibbs sampler (Gelfand and Smith, 1990). Also, we compare logit model with probit model using Bayes factor which is approximated by importance sampling method. One example is presented.

• Communications for Statistical Applications and Methods
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• 제23권3호
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• pp.231-239
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• 2016

A graphical diagnostic method based on multiple case deletions in a regression context is introduced by using the sampling distribution of the difference between two least squares estimators with and without multiple cases. Principal components analysis plays a key role in deriving this diagnostic method. Multiple case deletions of test statistic are also considered when a new observation is fitted to a given regression model. The result is useful for detecting influential observations in econometric data analysis, for example in checking whether the consumption pattern at a later time is the same as the one found before or not, as well as for investigating the influence of cases in the usual regression model. An illustrative example is given.

• 품질경영학회지
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• 제21권1호
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• pp.161-170
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• 1993

This study attempts to present the linear regression analysis that involves more than one regressor variable, because regression analysis is the most widely used statistical technique for describing, predicting and estimating the relationships between given data. The model of multiple linear regression may be solved directly by the two linear programming methods, i.e., to minimize the sum of the absolute deviation (MSD) and to minimize the maximum deviation(MMD). In addition, some results was compared to each techniques for accuracy and tested to the validity of statistical meaning.

• 응용통계연구
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• 제23권6호
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• pp.1191-1199
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• 2010

Knowing what factors into a player's ability to affect the outcome of a sports game is crucial. This knowledge helps determine the relative degree of contribution by each team member as well as sets appropriate annual salaries. This study uses statistical analysis to investigate how much the outcome of a professional baseball game is influenced by the records of individual players. We used the Lotte Giants' data on 252 games played between 2007 and 2008 that included environmental data(home or away games and opponents) as well as pitchers' and batters' data. Using a SAS Enterprise Miner, we performed a logistic regression analysis and decision tree analysis on the data. The results obtained through the two analytic methods are compared and discussed.

• 산업경영시스템학회지
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• 제35권3호
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• pp.118-128
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• 2012

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, 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. Meanwhile, 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 means in estimating related confidence intervals and testing the significance of model parameters. Based on simulated data, the efficiencies of estimates are ensured with a view to demonstrate the usefulness of the methodology.