• Journal of the Korean Home Economics Association
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• v.35 no.2
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• pp.283-296
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• 1997

The purpose of this study was to develope a housing suitable for the Korean elderly by analyzing traditional minds on housing of the middle-aged. Data were collected by the questionnaire from 696 middle-aged in 40's and 50's living in Chinju area. Collected data were analyzed by using statistical methods of One-Way MANOVA, scheffe's test, and multiple regression analysis which were processed with SAS. Following are results. 1) Middle aged people had four main traditional factors in their minds: a propensity toward traditional indoor atmosphere, traditional housing space, traditional housing value and ceremony space & traditional life style. 2) Socio-demographically, a propensity toward traditional housing space and ceremony space & traditional life style were significantly different between sexes, but not by educational level and income. A propensity toward traditional indoor atmosphere and ceremony space & traditional life style were different between housing types. 3) However, there were a significant difference between variables such as sex, educational level, income and housing type in residential demands for the elderly life. 4) Sex, housing type, a propensity toward traditional housing space and traditional housing value influenced residential demands for the elderly life I indoor and outdoor environments, nearby facilities and available services all of which were minor items in the demand for the elderly life.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.327-336
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• 2001

This work concerns estimating a regression function, which is not linear, using aggregate data. In much of the empirical research, data are aggregated for various reasons before statistical analysis. In a traditional parametric approach, a linear estimation of the non-linear function with aggregate data can result in unstable estimators of the parameters. More serious consequence is the bias in the estimation of the non-linear function. The approach we employ is the kernel regression smoothing. We describe the conditions when the aggregate data can be used to estimate the regression function efficiently. Numerical examples will illustrate our findings.

• Nuclear Engineering and Technology
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• v.16 no.3
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• pp.155-160
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• 1984

Analyzed is the thermal margin of the Korea Nuclear Unit 1 (KNU-1) reactor core consisting of either 14 x 14 standard fuel assemblies (SFA) or optimized fuel assemblies (OFA). Employed for the analysis are two different thermal design methods; traditional and statistical thermal design method. Compared to the traditional design thermal method, the statistical thermal design method improves the core thermal margin utilizing best-estimate values for the core operating parameters combining their uncertainties in a statistical manner. Calculations are performed using a steady state and transient thermal-hydraulic analysis computer program, COBRA-IV-i. Calculated results show that the statistical thermal design method significantly improves the thermal margin and satisfies the core thermal design base of the KNU-1 SFA and OFA core. However, the thermal design base can not be met, if the traditional thermal design method is employed for the OFA role analysis.

• Journal of Preventive Medicine and Public Health
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• v.40 no.2
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• pp.108-113
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• 2007

When conducting large-scale cohort studies, numerous statistical issues arise from the range of study design, data collection, data analysis and interpretation. In genomic cohort studies, these statistical problems become more complicated, which need to be carefully dealt with. Rapid technical advances in genomic studies produce enormous amount of data to be analyzed and traditional statistical methods are no longer sufficient to handle these data. In this paper, we reviewed several important statistical issues that occur frequently in large-scale genomic cohort studies, including measurement error and its relevant correction methods, cost-efficient design strategy for main cohort and validation studies, inflated Type I error, gene-gene and gene-environment interaction and time-varying hazard ratios. It is very important to employ appropriate statistical methods in order to make the best use of valuable cohort data and produce valid and reliable study results.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.11
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• pp.4609-4615
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• 2014

Background: To investigate tumor inhibition effects and mechanisms of Angelica sinensis and Sophorae flavescentis ait decoction (ASSF) combined with diamine-dichloroplatinum (DDP). Materials and Methods: Bodyweight, tumor inhibition rate and q value were calculated for single ASSF or ASSF combined with DDP on H22 carcinoma xenograft KM mice. Biochemical methods for serum LDH, AST, ALT, and AKP, ELISA method for serum HIF-$1{\alpha}$, pathological assessemnt of thymus, immunohistochemistry detection of tumor tissue caspase3 and mutant p53 protein, and qRT-PCR detection of bax/ bcl-2 mRNA were applied. Results: Compared with DDP control group, the bodyweight increased in ASSF-DDP group (p<0.01). Tumor inhibition rates for DDP, ASSF, ASSF-DDP were 62.7%. 43.7% and 71.0% respectively, with a q value of 0.90. Compared with other groups, thymus of DDP control group had obvious pathological injury (p<0.01), serum LDH, AST, ALT, AKP increased significantly in DDP control group (p<0.01), while serum HIF-$1{\alpha}$ was increased in the model control group. Compared with this latter, the expression of mutant p53 protein and bcl-2 mRNA were decreased in all treatment groups (p<0.01), but there were no statistical difference between DDP control p and ASSF-DDP groups. The expression of caspase3 protein and bax mRNA was increased in all treatment groups, with statistical differences between the DDP and ASSF-DDP groups (p<0.01). Conclusions: ASSF can inhibit bodyweight decrease caused by DDP, can inhibit tumor growth synergistically with DDP mainly through increasing serum HIF-$1{\alpha}$ and pro-apoptotic molecules such as caspase 3 and bax, rather than through decreasing anti-apoptotic mutant p53 and bcl-2. ASSF can reduce DDP toxicity due to decreasing the release of LDH, AST, ALT, AKP into blood and enhancing thymus protection.

• Journal of Acupuncture Research
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• v.32 no.4
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• pp.29-36
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• 2015

Objectives : The purpose of this study is to compare the efficacy of fire needling therapy and general acupuncture for the patient with lumbar herniated intervertebral disc. Methods : We observed 20 lumbar herniated intervertebral disc patients from March, 2014 to October, 2015. This study was randomized and assessor blinded. The patients were divided into two groups: one group(group A) was treated by general acupuncture only, and the other group(group B) was treated by fire needling therapy and general acupuncture. We evaluated the treatment effect experienced by each group using the visual analogue scale(VAS) and Oswestry disability index(ODI). Results : 1. In group A, VAS and ODI from before treatment to treatment 4, 8, 12, 16 were significantly improved in statistics. 2. In group B, VAS from before treatment to treatment 4, 8, 12, 16 was significantly improved, but ODI was significantly improved in statistics only from before treatment to treatment 12, not in treatment 4, 8, 16. 3. As a result of comparisons between group A and B, there were no statistical significance in VAS and ODI. Conclusions : This study was not able to determine statistical significance between the group treated by general acupuncture only and the group treated by fire needling therapy and general acupuncture, so further studies are required in the future.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.545-558
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• 2022

The purpose of this study is to explore a prediction model for accurately predicting Financial Difficulties of Chinese Cultural Industry Enterprises through Traditional Statistics and Machine Learning. To construct the prediction model, the data of 128 listed Cultural Industry Enterprises in China are used. On the basis of data groups composed of 25 explanatory variables, prediction models using Traditional Statistical such as Discriminant Analysis and logistic as well as Machine Learning such as SVM, Decision Tree and Random Forest were constructed, and Python software was used to evaluate the performance of each model. The results show that the Random Forest model has the best prediction performance, with an accuracy of 95%. The SVM model was followed with 93% accuracy. The Decision Tree model was followed with 92% accuracy.The Discriminant Analysis model was followed with 89% accuracy. The model with the lowest prediction effect was the Logistic model with an accuracy of 88%. This shows that Machine Learning model can achieve better prediction effect than Traditional Statistical model when predicting financial distress of Chinese cultural industry enterprises.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.545-557
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• 2018

The inclusion of covariates in the model often affects not only the estimates of meaningful variables of interest but also its statistical significance. Such gap between statistical and subject-matter significance is a critical issue in huge sample studies. A popular huge sample study, the sample cohort data from Korean National Health Insurance Service, showed such gap of significance in the inference for the effect of obesity on cause of mortality, requiring careful consideration. In this regard, this paper proposes a sample size calibration method based on a Monte Carlo t (or z)-test approach without Monte Carlo simulation, and also proposes a test procedure for subject-matter significance using this calibration method in order to complement the deflated p-value in the huge sample size. Our calibration method shows no subject-matter significance of the obesity paradox regardless of race, sex, and age groups, unlike traditional statistical suggestions based on p-values.

• Communications for Statistical Applications and Methods
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• v.18 no.3
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• pp.333-342
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• 2011

Many students in introductory statistics courses do not engage in learning under traditional classroom settings. A statistics instructor is often irritated by student behaviors such as sleeping, talking out of place, and acting bored or apathetic during lectures. The lecture and exercises in the computer laboratory should constantly compete with materials via the Internet to draw the attention of the student. To address problems in statistics education, we propose a digital nervous system in which a teacher and students can communicate with each other.

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.112-121
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• 1995

Let $p_1$ and $p_2$ be the proportions of two populations. To test the hypothesis $H_0 : p_1 = p_2$, we usually use the $x^2$ statistic, the large sample binomial statistic Z, and the Generalized Likelihood Ratio statistic-2log $\lambda$developed based on different mathematical rationale, respectively. Since testing the above hypothesis is equivalent to testing whether two populations follow the common Bernoulli distribution, one may also test the hypothesis by comparing 1 with the ratio of each density estimate and the hypothesized common density estimate, called comparison density, which was devised by Parzen(1988). We show that the above traditional test statistics ate actually estimating the measure of distance between the true densities and the common density under $H_0$ by representing them with the comparison density.