• 아태비즈니스연구
• /
• 제14권1호
• /
• pp.365-379
• /
• 2023

Purpose - Our previous study (Chang & Lee, 2023) presented observations on the recent changes in the year-on-year (YoY) Consumer Price Index (CPI) of the Republic of Korea (ROK) after the COVID-19 pandemic. The purpose of this article is to present an integrated analysis and interpretation of the recent changes in CPI and the Aggregate Import Price Index (IPI) by incorporating recent data, specifically data from September 2022 to December 2022. Design/methodology/approach - This study collected CPI (YoY) data in the ROK from January 2019 to December 2022 using e-National Indicator System provided by the ROK. Statistical analysis was employed to analyze the data. Findings - First, we confirm the extended results of the existing study by Chang and Lee (2023). Second, we demonstrate that the Aggregate IPI in ROK increased significantly in 2022 compared to 2021. We then provide an integrated interpretation on the significant increase in CPI and aggregate IPI in ROK, which complements Chang and Lee (2023) that limits their discussion to YoY CPI. Moreover, we show that the IPI of the semiconductor in ROK decreased significantly in 2022 compared to 2021. Research implications or Originality - Our results provide important insights into the recent changes in the CPI in the ROK. The results suggest that these changes can be partially attributed to various factors, such as the global supply chain disruptions resulting from the spread of the COVID-19 pandemic and the prolonged war between Russia and Ukraine, the side effect of quantitative easing by the US Federal Reserve, heat waves and droughts caused by climate change in ROK, a surge in demand following a gradual daily recovery, US-China trade conflict, etc. Our study shows statistically comprehensive results compared to the studies that limit their discussion to YoY average growth rate.

• Communications for Statistical Applications and Methods
• /
• 제8권3호
• /
• pp.643-650
• /
• 2001

Some classical plots like scatterplot matrices and parallel coordinates are valuable tools for data visualization. These tools are extensively used in the modern data mining softwares to explore the inherent data structure, and hence to visually classify or cluster the database into appropriate groups. However, the interpretation of these plots are very sensitive to the arrangement of variables. In this work, we introduce two methods to arrange the variables for data visualization. First method is based on the work of Wegman (1999), and this is to arrange the variables using minimum distance among all the pairwise permutation of the variables. Second method is using the idea of principal components. We Investigate the effectiveness of these methods with parallel coordinates using real data sets, and show that each of the two proposed methods has its own strength from different aspects respectively.

• Journal of Periodontal and Implant Science
• /
• 제45권1호
• /
• pp.2-7
• /
• 2015

A fundamental problem in analyzing complex multilevel-structured periodontal data is the violation of independency among the observations, which is an assumption in traditional statistical models (e.g., analysis of variance and ordinary least squares regression). In many cases, aggregation (i.e., mean or sum scores) has been employed to overcome this problem. However, the aggregation approach still exhibits certain limitations, such as a loss of power and detailed information, no cross-level relationship analysis, and the potential for creating an ecological fallacy. In order to handle multilevel-structured data appropriately, mixed effects models have been introduced and employed in dental research using periodontal data. The use of mixed effects models might account for the potential bias due to the violation of the independency assumption as well as provide accurate estimates.

• 한국수학교육학회지시리즈D:수학교육연구
• /
• 제16권4호
• /
• pp.265-276
• /
• 2012

These days, with the emphasis on statistical literacy, the importance of communication is the focus of attention. Communication about statistics is important since it is a way of describing the understanding of concepts and the interpretation of data. However, students usually have trouble with expressing what they understand, especially through writing. In this paper, we examined preservice teachers' difficulties when they wrote about statistical concepts. By comparing preservice teachers' written responses and interview transcripts of the variance concept task, we could find the missing information in their written language compared to their verbal language. From the results, we found that preservice teachers had difficulty in connecting terms contextually and conceptually, presenting various factors of the concepts that they considered, and presenting the problem solving strategies that they used.

• 한국지구과학회지
• /
• 제25권3호
• /
• pp.152-159
• /
• 2004

The purpose of this study was to explore characteristics of natural history museum exhibits from the viewpoint of science education. A total of ninety exhibits for this study were examined in national science museums of Korea and Japan. Exhibits of Tokyo national science museum were again divided into two groups: the old and traditional types, and the new and renovated ones. Even though analyzing data was not undertaken through quantitative statistical process, the interpretation of the data was valid enough to fulfill the purpose of the research. While there were clear changes and differences between the old and the new types of exhibits in Tokyo national science museum, the old part of Tokyo museum was similar to one in Korea. Based on analyzing the new types of Tokyo museum, the current movement in the field of natural history museums of Korea explicitly has toward utilizing more science education concepts and ideas.

• Journal of the Korean Statistical Society
• /
• 제30권4호
• /
• pp.613-635
• /
• 2001

This paper proposes a skewed multivariate probit model for analyzing a correlated binary response data with covariates. The proposed model is formulated by introducing an asymmetric link based upon a skewed multivariate normal distribution. The model connected to the asymmetric multivariate link, allows for flexible modeling of the correlation structure among binary responses and straightforward interpretation of the parameters. However, complex likelihood function of the model prevents us from fitting and analyzing the model analytically. Simulation-based Bayesian inference methodologies are provided to overcome the problem. We examine the suggested methods through two data sets in order to demonstrate their performances.

• The Korean Journal of Pain
• /
• 제23권1호
• /
• pp.35-41
• /
• 2010

Background: Statistical analysis is essential in regard to obtaining objective reliability for medical research. However, medical researchers do not have enough statistical knowledge to properly analyze their study data. To help understand and potentially alleviate this problem, we have analyzed the statistical methods and errors of articles published in the Korean Journal of Pain (KJP), with the intention to improve the statistical quality of the journal. Methods: All the articles, except case reports and editorials, published from 2004 to 2008 in the KJP were reviewed. The types of applied statistical methods and errors in the articles were evaluated. Results: One hundred and thirty-nine original articles were reviewed. Inferential statistics and descriptive statistics were used in 119 papers and 20 papers, respectively. Only 20.9% of the papers were free from statistical errors. The most commonly adopted statistical method was the t-test (21.0%) followed by the chi-square test (15.9%). Errors of omission were encountered 101 times in 70 papers. Among the errors of omission, "no statistics used even though statistical methods were required" was the most common (40.6%). The errors of commission were encountered 165 times in 86 papers, among which "parametric inference for nonparametric data" was the most common (33.9%). Conclusions: We found various types of statistical errors in the articles published in the KJP. This suggests that meticulous attention should be given not only in the applying statistical procedures but also in the reviewing process to improve the value of the article.

• Communications for Statistical Applications and Methods
• /
• 제28권4호
• /
• pp.369-391
• /
• 2021

The vlda is an R (R Development Core team et al., 2011) package which provides functions for visualization of multidimensional longitudinal data. In particular, the R package vlda was developed to assist in producing a plot that more effectively expresses changes over time for two different types (long format and wide format) and uses a consistent calling scheme for longitudinal data. The main features of this package allow us to identify the relationship between categories and objects using an indicator matrix with object information, as well as to cluster objects. The R package vlda can be used to understand trends in observations over time in addition to identifying relative relationships at a simple visualization level. It also offers a new interactive implementation to perform additional interpretation, therefore it is useful for longitudinal data visual analysis. Due to the synergistic relationship between the existing VLDA plot and interactive features, the user is empowered by a refined observe the visual aspects of the VLDA plot layout. Furthermore, it allows the projection of supplementary information (supplementary objects and variables) that often occurs in longitudinal data of graphs. In this study, practical examples are provided to highlight the implemented methods of real applications.

• Journal of Hospice and Palliative Care
• /
• 제13권1호
• /
• pp.1-6
• /
• 2010

임상시험은 인간을 대상으로 약물 또는 치료법의 효과를 검증하는 것을 목적으로 하고 있다. 성공적인 임상시험을 위해서는 단순한 자료분석에만 통계의 이용을 제한하지 않고 다양한 영역으로 활용의 폭을 넓히는 것이 필요하다. 연구계획단계에서부터 구체적이고 체계적으로 통계의 활용을 고려하기 위해 효과에 대한 정의, 적정한 표본크기 산정, 통계분석 방법 등 전반적인 통계의 응용을 고찰한다.

• 응용통계연구
• /
• 제24권3호
• /
• pp.445-453
• /
• 2011

Almost all small area estimations are obtained by minimizing the mean squared error. Recently relative error prediction methods have been developed and adapted to small area estimation. Usually the estimators obtained by using relative error prediction is called a shrinkage estimator. Especially when data set consists of large range values, the shrinkage estimator is known as having good statistical properties and an easy interpretation. In this paper we study the shrinkage estimators based on logistic regression type estimators for small area estimation. Some simulation studies are performed and the Economically Active Population Survey data of 2005 is used for comparison.