• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.649-665
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• 2007

The improvement of quality is a continuous process and one of the main objectives of the Statistical Strategy launched by the Korea National Statistical Office (KNSO) is the enhancement of the quality of Korea national statistics. In this paper, we define the processing statistic, classify the Korea national processing statistics, and develop the quality indicators and check list for assessing the national processing statistics of Korea. During its development, the indicators has been discussed with the processing statistic managers of the KNSO and the checklist tested in a pilot study covering a variety of processing statistic areas.

• Journal of Environmental Policy
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• v.2 no.2
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• pp.81-103
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• 2003

This paper compares the environmental economic performance of the South Korean and the German economy during the last decade. The analysis is based on comparable data from the Environmental Economic Accounts (EEA). The EEA is a satellite account to the National Accounts which enhances the conventional economic accounts by a description of the interactions between the economy and the environment. The data from the EEA and the national accounts are fully compatible. In absolute terms the environmental pressures caused by economic activities were with regards to the environmental factors used for the analysis generally lower in South Korea than in Germany. If the use of environmental factors is related to each country's gross domestic product (environmental productivities) a lower level of environmental productivity can be observed for most of the environmental factors in South Korea compared to Germany. For example in 1999 energy and $CO_2$ productivity were about two fifths of the German level. This corresponds to the relation regarding labour productivity (Gross domestic product per employment).

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2009.05a
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• pp.153-159
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• 2009

The purpose of this paper is to suggest the problems of basic population data(1960-2005) and the data(2006-2050) of population projections reported by Korean National Statistical Office in November 2006. The errors on the basic population data can be easily checked by using the graphical analysis and the method of linear regression analysis. It is necessary to revise the population projections reported by Korean National Statistical Office.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.849-860
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• 2005

This Paper analyzed activities of the average time use survey in 1999 and 2004 by Korea National Statistical Office. The survey of National Statistical Office considered general analysis for activities like that personal care(eating), employment and study, leisure, travel by purpose. Change of time of living causes a lot of effects on society whole economy, culture etc., and is used basic data in policy proposal of the country and enterprise's marketing operation establishment. Analysis for average time could look at change in a role of gender, holiday of saturday's enforcement and increasing of temporary post. Using this analysis we can do more developed and analytical sociological study.

• Proceedings of the Korean Association for Survey Research Conference
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• 2003.06a
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• pp.69-84
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• 2003

Main monthly household surveys conducted by National Statistical Office are economically active population survey and household expenditure survey. Samples of these two surveys are redesigned every 5 years based on Census. This paper is about sample redesign of household survey conducted in 2002 based on 2000 Census.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.737-749
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• 2003

This Paper analyzed simultaneous activities of the time use survey by Korea National Statistical Office to use data mining's association rule. The survey of National Statistical Office in 1999 considered general analysis for main activities like that personal care(eating), employment and study, leisure, travel by purpose. But if we use the association rule, we can found the ratio of simultaneous activities at the same time. And also we can found the probability that another activities practise if we act one particular activity. Using this association rule of data mining we can do more developed and analytical sociological study.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.159-170
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• 2003

This Paper analyzed simultaneous activities of the time use survey by Korea National Statistical Office to use data mining‘s association rule. The survey of National Statistical Office in 1999 considered general analysis for simultaneous activities. But if we use the association rule, we can found the ratio of particular activities at the same time. And we found the probability that another activities practise if we act one particular activity. Using this association rule of data mining we can do more developed and analytical sociological study.

• The Journal of Industrial Distribution & Business
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• v.12 no.9
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• pp.7-18
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• 2021

Purpose: This study aims to investigate whether there is a difference in the prevalence of hypertension according to gender, occupational group, and occupational group according to gender. Research design, data, and methodology: This study classified the occupational groups according to the gender of men and women between the ages of 20 and 49 into office workers and non-office workers from the 7th 2017 National Health and Nutrition Examination Survey. A total of 2,691 people were surveyed, including 1,394 office workers and 1,297 non-office workers. Frequency analysis, chi-square, and Independent T-test for distribution difference analysis of categorical variables analysis for occupation and hypertension were applied. Statistical significance was verified at 0.001 to determine the validity analysis. All statistical analyses were performed using the IBM SPSS 24.0 program. Results: The main risk factors for hypertension were gender, age, education, obesity, smoking, drinking, family history, and chronic diseases. There were differences in the prevalence of hypertension among office workers and non-office workers. Conclusions: Men had a higher prevalence of hypertension than women, while non-office workers had a higher prevalence than office workers. In the case of women, non-office workers have a higher prevalence of hypertension than white-collar workers.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.575-583
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• 2003

We proposed imputation strategies for the Population and Housing Census 2000 in Korea. The total area of floor space and marital status which have relatively high non-response rates in the Census are considered to develope the effective missing value imputation procedures. The Classification and Regression Tree(CART) is employed to construct the imputation cells for hot-deck imputation, as well as to predict missing value by model-based approach. We compare three imputation methods which include CART model-based imputation, hot-deck imputation based on CART and logical hot-deck imputation proposed by The Korea National Statistical Office. The results suggest that the proposed hot-deck imputation based on CART is very efficient and strongly recommendable.

• Journal of Wellbeing Management and Applied Psychology
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• v.4 no.1
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• pp.11-18
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• 2021

Purpose: This study aims to explore the correlation between job and hypertension. Hypertension is a major risk factor such as stroke, myocardial infarction, heart failure, and vascular aneurysms. Research design, data and methodology: The data from this study are the 2017 National Health and Nutrition Examination Survey classified into office workers and non-office workers. Frequency analysis was used for general characteristics analysis, and ANOVA test analysis was used for correlation between subjects' basic variables and blood pressure. The post analysis as turey was used to verify the validity of the statistical results. All statistical analyzes were performed using the IBM SPSS 24.0 program. Results: There was a difference in the prevalence of hypertension among office workers and non-office workers.