• Journal of dental hygiene science
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• v.11 no.2
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• pp.63-67
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• 2011

This study aimed to propose appropriate methods of statistical analysis and interpretation of survey results in dental hygiene. By using examples of published articles in the Journal of Dental Hygiene Science from 2003 to 2008, we made some suggestions to improve scientific and clear writing of the contents; purpose of the study, subjects and methods, statistical analysis, tables, and interpretations.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.495-509
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• 2013

We study multivariate extension of Kendall's tau and its statistical interpretation. There exist various versions of multivariate Kendall's tau, for example Scarsini (1984), Joe (1990) and Genest et al. (2011); however, few of them mention its lower bounds. For the bivariate case, the Fr$\acute{e}$chet-Hoeffding lower bound can achieve the lower bound of Kendall's tau. However in the multivariate case, the Fr$\acute{e}$chet-Hoeffding lower bound itself does not exist as a distribution, which makes the interpretation of Kendall's tau unclear when it has negative value. In this paper, we explain sufficient conditions to achieve the lower bound of Kendall's tau and provide real data examples that provide further insights into the interpretation for the lower bounds of Kendall's tau.

• Journal of the Korean Society of Radiology
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• v.84 no.4
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• pp.866-878
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• 2023

Statistical analysis is an essential component of the medical writing process for research-related articles. Although the importance of statistical testing is emphasized, statistical mistakes continue to appear in journal articles. Major statistical mistakes can occur in any of the three different stages of medical writing, including in the design stage, analysis stage, and interpretation stage. In the design stage, mistakes occur if there is a lack of specificity regarding the research hypothesis or data collection and analysis plans. Discrepancies in the analysis stage occur if the purpose of the study and characteristics of the data are not sufficiently considered, or when an inappropriate analytic procedure is followed. After performing the analysis, the results are interpreted, and an article is written. Statistical analysis mistakes can occur if the underlying methods are incorrectly written or if the results are misinterpreted. In this paper, we describe the statistical mistakes that commonly occur in medical research-related articles and provide advice with the aim to help readers reduce, resolve, and avoid these mistakes in the future.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.651-659
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• 2001

Preference map is a widely used graphical method for the preference data set which is frequently encountered in the field of marketing research. This provides joint configuration usually in two dimensional space between "products" and their "attributes". Whereas the classical preference map adopts the ordinary least squares method in deriving map, the present article suggests the weighted least squares approach providing the better graphical display and interpretation compared to the classical one. Internet search engine data in Korea are analysed for illustration.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.170-180
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• 2010

With development of the earth's subsurface space, the need for a reliable subsurface spatial model such as a cross-section, boring log is increasing. However, the ground mass was essentially uncertain. To generate model was uncertain because of the shortage of data and the absence of geotechnical interpretation standard(non-statistical uncertainty) as well as field environment variables(statistical uncertainty). Therefore, the current interpretation of the data and the generation of the model were accomplished by a highly trained experts. In this study, a geotechnical ontology model was developed using the current expert experience and knowledge, and the information content was calculated in the ontology hierarchy. After the relative distance between the information contents in the ontology model was combined with the distance between cluster centers, a cluster analysis that considered the geotechnical semantics was performed. In a comparative test of the proposed method, k-means method, and expert's interpretation, the proposed method is most similar to expert's interpretation, and can be 3D-GIS visualization through easily handling massive data. We expect that the proposed method is able to generate the more reasonable subsurface spatial information model without geotechnical experts' help.

• Mass Spectrometry Letters
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• v.3 no.3
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• pp.63-67
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• 2012

The need of heavy and unconventional crude oil as an energy source is increasing day by day, so does the importance of petroleomics: the pursuit of detailed knowledge of heavy crude oil. Crude oil needs techniques with ultra-high resolving capabilities to resolve its complex characteristics. Therefore, ultra-high resolution mass spectrometry represented by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) has been successfully applied to the study of heavy and unconventional crude oils. The analysis of crude oil with high resolution mass spectrometry (FT-ICR MS) has pushed analysis to the limits of instrumental and methodological capabilities. Each high-resolution mass spectrum of crude oil may routinely contain over 50,000 peaks. To visualize and effectively study the large amount of data sets is not trivial. Therefore, data processing and visualization methods such as Kendrick mass defect and van Krevelen analyses and statistical analyses have played an important role. In this regard, it will not be an overstatement to say that the success of FT-ICR MS to the study of crude oil has been critically dependent on data processing methods. Therefore, this review offers introduction to peotroleomic data interpretation methods.

• East Asian mathematical journal
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• v.37 no.4
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• pp.481-497
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• 2021

The importance of statistics in everyday life and work place has led to calls for an increased attention to statistical literacy in the mathematics curriculum both internationally and domestically. While professional organizations and researchers propose perspectives towards and models of statistical literacy, conceptions and elements of statistical literacy vary. This study examines how mathematics textbook questions fulfill the requirements of statistical literacy by employing two models: Watson's model focusing on understanding of statistical language and Curcio's model on data interpretation aspects of statistical literacy. For this, a total of 872 problem questions presented in the statistics units of from ten textbooks for the middle school year 1 mathematics were analyzed.

• Proceedings of the Korea Concrete Institute Conference
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• 1999.04a
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• pp.659-664
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• 1999

The majority of published conclusions about structural configuration effects of bond strength were based on the observed performance of test specimens and their interpretations are mostly empirical and statistical. The empirical and statistical interpretation on bond strength have to be replaced by rational models based on simple, sound and verifiable mechanical principles. It is likely that such models also represent the key to a deeper understanding of some existing experimental data on bond strength. The presented truss model is capable of explaining failure modes involving bond slip that cannot be explained by current truss model.

• Journal of Elementary Mathematics Education in Korea
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• v.24 no.1
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• pp.1-30
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• 2020

This study was carried out in order to identify the error types of statistical graphs for statistical literacy education. We analyze the meaning of using graphs in statistical problem solving, and identify categories, frequencies, and contexts as the components of statistical graphs. Error types of representing categories and frequencies make statistics consumers see incorrect distributions of data by subjective point of view of statistics producers and visual illusion. Error types of providing contexts hinder the interpretation of statistical information by concealing or twisting the contexts of data. Moreover, the findings show that tasks provide standardized frame already for drawing graphs in order to avoid errors and pay attention to the process of drawing the graph rather than statistical literacy for analyzing data. We suggest some implications about statistical literacy education, ethical problems, and knowledge for teaching to be considered when teaching the statistical graph in elementary mathematics classes.

• The Mathematical Education
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• v.58 no.3
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• pp.459-481
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• 2019

This study aims to investigate students' statistical thinking through statistical processes in different instructional settings: Teacher-centered instruction vs. student-centered learning. We first developed instructional materials that allowed students to experience all the processes of statistics, including data collection, data analysis, data representation, and interpretation of the results. Using the instructional materials for four classes, we collected and analyzed the data from 57 seventh graders' discourse and artifacts from two different instructional settings using the analytic framework generated on the basis of literature review. The results showed that students felt difficulty particularly in the process of data collection and graph representations. In addition, even though data description has been heavily emphasized for data analysis in statistics education, it is surprisingly discovered that students had a hard time to understand the relationship between data and representations. Also, there were relationships between students' statistical thinking and instructional settings. Even though both groups of students showed difficulty in data collection and graph representations of the data, there were significant differences between the groups in terms of their performance. Whereas students from student-centered learning class outperformed in making decisions considering verification and justification, students from teacher-centered lecture class did better in problems requiring accuracy than the counterpart. The results from the study provide meaningful implications on developing curriculum and instructional methods for statistics education.