• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.335-340
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• 2003

최근 들어 생존분석 기법이 여러 분야에서 관심을 모으고 있을 뿐 아니라 생존자료를 분석하기 위한 여러 패키지들도 개발되어 연구되고 있다. 본고에서는 생존분석의 여러 모형을 간략히 소개하고 생존자료를 분석하기 위하여 널리 사용되고 있는 패키지인 SAS, SPSS, STATA의 기능을 찾아보고 그들의 특징을 비교 조사할 것이다.

• International Journal of Highway Engineering
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• v.7 no.2 s.24
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• pp.77-86
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• 2005

A spalling model for jointed connote pavements(JCP) was developed using the data of distresses, climates, and materials at the sections of long-term pavement performance(LTPP) investigation. The total of forty-four spatting distress data was collected at twenty-two JCP LTPP sections in 1999 and in 2004. In addition, the data of climates, geometric shapes of the slabs, and material properties was also collected at those times. Factors significantly influencing the spalling distress were found by a sensitivity analysis for the data. Consequently, a spatting model was developed by a multi-regression analysis for the factors. The model showed reasonable trend of the spatting development comparing to other foreign models.

• Journal of the Korea Computer Industry Society
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• v.10 no.5
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• pp.213-220
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• 2009

Regression analysis to estimate the fitted models and test hypotheses are basic statistical tools for survey data as well as experimental data. Data is collected as pairs of independent and dependent variables, and statistics are computed using matrix calculation. To estimate a best fitted model is a key to maximize reliability of regression analysis. To fit a regression model, plot data on XY axis and select the most fitted models. Researchers estimate the best model and test hypothesis with MS Excel's graph menu and matrix computation functions. In this study, I develop macros to estimate the fitted regression model and test hypotheses of relationship between variables. Subway electric charges data with one dependent variable and three independent variables are tested using developed macros, and compared with the results using built-in Excel of regression analysis.

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.103-111
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• 2001

Now a day, overseas tour which is due to economic development grows very much. In this situation, a forecast of overseas tour is required to establish tourism policy for tourism marketing. In this paper, we compare regression model and gravity model for a forecast of overseas tour. Using gravity model, this study also suggests an attraction which is suitable to our situation, and suggested attraction is compared and analyzed with another.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.567-573
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• 2011

This study develops a predictive model for household water end-uses based on data that have measured household characteristics, housing characteristics and other items, surveyed over 3 years in Korea. However, the measured data was left-skewed and it was not fitted to normal distribution. The parameter estimate were biased when using a multiple regression model. In addition, the results of the testing for the model were usually of significance due to the tiny residual from a large number of observations. In order to solve the problem, we suggested log-normal regression model and Weibull regression model as alternatives. The results of this study can be utilized in the planning stages of water and waste water facilities.

• Survey Research
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• v.5 no.2
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• pp.49-70
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• 2004

This paper is a broad review about general regression estimators, which are very useful when auxiliary variables are available in survey sampling. We investigate the process of development of general regression estimators from birth to suggestion of variance estimation method and examine some properties of general regression estimators by comparing with calibration and QR estimators. We also present some forms of general regression estimators available under complex sampling designs such as stratified sampling and cluster sampling. Finally, we comment some advantages as well as disadvantages of general regression estimators and theoretical and practical development in the future.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.47-59
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• 2020

In this paper, we present the longitudinal Markov binary regression model with t-link function when its transition order is known or unknown. It is assumed that logit or probit models are considered in binary regression models. Here, t-link function can be used for more flexibility instead of the probit model since the t distribution approaches to normal distribution as the degree of freedom goes to infinity. A Markov regression model is considered because of the longitudinal data of each individual data set. We propose Bayesian method to determine the transition order of Markov regression model. In particular, we use the deviance information criterion (DIC) (Spiegelhalter et al., 2002) of possible models in order to determine the transition order of the Markov binary regression model if the transition order is known; however, we compute and compare their posterior probabilities if unknown. In order to overcome the complicated Bayesian computation, our proposed model is reconstructed by the ideas of Albert and Chib (1993), Kuo and Mallick (1998), and Erkanli et al. (2001). Our proposed method is applied to the simulated data and real data examined by Sommer et al. (1984). Markov chain Monte Carlo methods to determine the optimal model are used assuming that the transition order of the Markov regression model are known or unknown. Gelman and Rubin's method (1992) is also employed to check the convergence of the Metropolis Hastings algorithm.

• Survey Research
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• v.10 no.3
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• pp.1-19
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• 2009

This study investigates the utilities of spatial analysis in the context of survey research using Geographical Information System(GIS) and Geographically Weighted Regression (GWR) which take account of spatial heterogeneity. Many social phenomena involve spatial dimension, and with the development of GIS, GPS receiver, and online location-based services, spatial information can be collected and utilized more easily, and thus application of spatial analysis in the survey research is getting easier. The traditional OLS regression models which assume independence of observations and homoscedasticity of errors cannot handle spatial dependence problem. GWR is a spatial analysis technique which utilizes spatial information as well as attribute information, and estimated using geographically weighted function under the assumption that spatially close cases are more related than distant cases. Residential survey data from a Primary Autonomous District are used to estimate a model of public service satisfaction. The findings show that GWR handles the problem of spatial auto-correlation and increases goodness-of-fit of model. Visualization of spatial variance of effects of the independent variables using GIS allows us to investigate effects and relationships of those variables more closely and extensively. Furthermore, GIS and GWR analyses provide us a more effective way of identifying locations where the effect of variable is exceptionally low or high, and thus finding policy implications for social development.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.859-873
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• 2008

Job satisfaction impacts on the turnover intention of employee, which affects the turnover behavior. This paper concerns with the impact of job satisfaction on the turn over behavior. Since turnover intention is highly correlated with job satisfaction, salary, employment status and etc, we should pay careful attention for modelling of those variables as independent variables and the turnover behavior as a dependent variable in the empirical study for the impact of factors on turnover behavior. We detect significant variables which effect the turnover behavior using 2-stage logistic regression inserting the turnover intention, an independent variable, with the chance estimates derived from the instrumental variables in Graduates Occupational Mobility Survey.

• The Journal of the Convergence on Culture Technology
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• v.6 no.1
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• pp.111-120
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• 2020

As an application of big data and artificial intelligence techniques, this study proposes an atypical language-based sentimental opinion poll methodology, unlike conventional opinion poll methodology. An alternative method for the sentimental classification model based on existing statistical analysis was to collect real-time Twitter data related to parliamentary elections and perform empirical analyses on the Polarity and Intensity of public opinion using attribute-based sensitivity analysis. In order to classify the polarity of words used on individual SNS, the polarity of the new Twitter data was estimated using the learned Lasso and Ridge regression models while extracting independent variables that greatly affect the polarity variables. A social network analysis of the relationships of people with friends on SNS suggested a way to identify peer group sensitivity. Based on what voters expressed on social media, political opinion sensitivity analysis was used to predict party approval rating and measure the accuracy of the predictive model polarity analysis, confirming the applicability of the sensitivity analysis methodology in the political field.