• Communications for Statistical Applications and Methods
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• v.15 no.3
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• pp.303-324
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• 2008

From mid-1890's, biometricians and Mendelians debated over Darwin's evolutionary theory. Biologist W. Weldon and Mathematician K. Pearson were leaders of the biometric school and biologist W. Bateson led Mendelian school. In this paper topics of the controversy such as causation vs. correlation, frequency distribution are considered. And in relation to the tradition of British statistics, we consider the philosophy of Karl Pearson revealed in this debate. Besides many statistical methods and concepts by Karl Pearson, the newly born mathematical statistics got a new journal Biometrika, a department in university, and a school of researchers from this controversy.

• Physical Therapy Korea
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• v.28 no.3
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• pp.161-167
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• 2021

Understanding sciences behind fall-related hip fractures in older adults is important to develop effective interventions for prevention. The aim of this review is to provide biomechanical understanding and prevention strategies of falls and related hip fractures in older adults, in order to guide future research directions from biomechanical perspectives. While most hip fractures are due to a fall, a few of falls are injurious causing hip fractures, and most falls are non-injurious. Fall mechanics are important in determining injurious versus non-injurious falls. Many different biomechanical factors contribute to the risk of hip fracture, and effects of each individual factors are known well. However, combining effects, and correlation and causation among the factors are poorly understood. While fall prevention interventions include exercise, vision correction, vitamin D intake and environment modification, injury prevention strategies include use of hip protectors, compliant flooring and safe landing strategies, vitamin D intake and exercise. While fall risk assessments have well been established, limited efforts have been made for injury risk assessments. Better understanding is necessary on the correlation and causation among factors affecting the risk of falls and related hip fractures in older adults. Development of the hip fracture risk assessment technique is required to establish more efficient intervention models for fall-related hip fractures in older adults.

• Journal of the Korean Society of Physical Medicine
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• v.4 no.1
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• pp.31-41
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• 2009

Purpose : Purpose of this study was to determine correlation between Walter Reed Visual Analogue Scale (WRVAS) and Scoliosis Research Society-22(SRS-22) questionnaire for idiopathic scoliosis. Methods : For this study, to assess reliability, 224 middle school female students were applied the WRVAS & SRS-22 questionnaire. Pearson correlation analysis was used to know correlationship between WRVAS and SRS-22 questionnaire. Regression analysis was used to know causation between WRVAS and SRS-22 questionnaire. Results Positive correlation(.248) was found between WRVAS score and SRS-22 score. And SRS-22 was shown positive correlation compared with WRVAS as a result slope of .078 Conclusion : The WRVAS questionnaire is reliable positive correlation with SRS-22 questionnaire. It is valid and may be useful for clinical evaluation of idiopathic scoliosis patients.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.285-299
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• 2020

In many cases, we are interested in identifying independence between variables. For continuous random variables, correlation coefficients are often used to describe the relationship between variables; however, correlation does not imply independence. For finite discrete random variables, we can use the Pearson chi-square test to find independency. For the mixed type of continuous and discrete random variables, we do not have a general type of independent test. In this study, we develop a independence test of a continuous random variable and a discrete random variable without assuming a specific distribution using kernel density estimation. We provide some statistical criteria to test independence under some special settings and apply the proposed independence test to Pima Indian diabetes data. Through simulations, we calculate false positive rates and true positive rates to compare the proposed test and Kolmogorov-Smirnov test.

• Journal of the Korea Safety Management & Science
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• v.8 no.2
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• pp.13-23
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• 2006

The present study has investigated the patterns and the causes of safety -accidents on the accident-data in semiconductor Industries through near miss report the cases in the advanced companies. The ratio of incomplete actions to incomplete state was 4 to 6 as the cases of accidents in semiconductor industries in the respect of Human-ware, Hard- ware, Environment-ware and System-ware. The ratio of Human to machine in the attributes of semiconductor accident was 4 to 1. The study also investigated correlation among the system related to production, accident, losses and time. In semiconductor industry, we found that pattern of safety-accident analysis is organized potential, interaction, complexity, medium. Therefore, this study find out that semiconductor model consists of organization, individual, task, machine, environment and system.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.395-395
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• 2000

Optimization of existing processes becomes more important than the past as environmental problems and concerns about energy savings stand out. When we can model a process mathematically, we can easily optimize it by using the model as constraints. However, modeling is very difficult for most chemical processes as they include numerous units together with their correlation and we can hardly obtain parameters. Therefore, optimization that is based on the process models is, in turn, hard to perform. Especially, f3r unknown processes, such as bioprocess or microelectronics materials process, optimization using mathematical model (first principle model) is nearly impossible, as we cannot understand the inside mechanism. Consequently, we propose a few optimization method using empirical model evolutionarily instead of mathematical model. In this method, firstly, designing experiments is executed fur removing unecessary experiments. D-optimal DOE is the most developed one among DOEs. It calculates design points so as to minimize the parameters variances of empirical model. Experiments must be performed in order to see the causation between input variables and output variables as only correlation structure can be detected in historical data. And then, using data generated by experiments, empirical model, i.e. response surface is built by PLS or MLR. Now, as process model is constructed, it is used as objective function for optimization. As the optimum point is a local one. above procedures are repeated while moving to a new experiment region fur finding the global optimum point. As a result of application to the pulp digester benchmark model, kappa number that is an indication fur impurity contents decreased to very low value, 3.0394 from 29.7091. From the result, we can see that the proposed methodology has sufficient good performance fur optimization, and is also applicable to real processes.

• Korean Security Journal
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• no.14
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• pp.261-283
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• 2007

In recent studies for explaining the causation of crime fear shows interest and effort in studies attempting microscopical individual level and macroscopical local level of sex, age, economic level, crime damage level and etc. However, in this study, it is considered that interest and analysis of individual on characteristics of these local level may has its difference depends on crime damage experience in the past, fragility precision of crime damage and interest on crime relating information and processed positive analysis on characteristics of individual and relation of crime fear on individual level before making an attempt of connecting microscopical level and macroscopical level. Therefore, the purpose of this study is on positive verification of how people feel about crime fear depends on individual's characteristic and also how much effect would they receive. As the result of this study, it is shown that first, population statistical characteristics that crime damage experience is statistically meaningful of its difference of each group are age, status of marriage, final education status and residential area and for the fragility precision of crime damage was sex and status of marriage and for the interest about the crime relating information has meaningful difference statistically of each group depends on sex, age, final education status, income of the house and location of residential area. Second, after processing correlation analysis on individual characteristic primary factor and crime fear, the result of 3 primary factor independent variable all shows statistically meaningful correlation with crime fear and especially fragility primary factor on crime damage showed the most high correlation with crime fear. Lastly, fragility of crime damage, interest on crime information and crime damage experience has effected as characteristics of individual and especially fragility of crime damage which the person thought to be the most fragility on crime damage out of these individual characteristic primary factor showed to have the most effecting primary factor.

• Journal of Navigation and Port Research
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• v.33 no.3
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• pp.181-191
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• 2009

In general, seafarer's human error is considered to be the preponderant muse for the majority of maritime transportation accidents in a merchant ship. The implementation techniques for Human Error Model (HEM) to assess possible accident risk by deck officers including captain, chief officer, second mate and third mate are described in this study. The scope of this work is focused to 642 deck officers in the ship management company with 130 vessels. At first, HEM can be constructed through the statistical analysis and expert's brainstorming process with human data to 642 deck officers. Then the variables $\upsilon$ for the human factors, the evaluation level EP($\upsilon$) for $\upsilon$, the weight $\alpha$ of $\upsilon$, and the title weight $\beta$ of each deck officers can be decided. In addition, through the analysis of ship's accident history, the accident causation ratios by human error ${\gamma}_H$ and by external error ${\gamma}_B$ can be found as 0.517(51.7%) and 0.483(48.3%), respectively. The correlation coefficients to $\upsilon$ are also shown significant for a 95% confidence interval (p < 0.05) for each coefficient. And the validity of HEM is also surveyed by the analysis of normal probability distribution of risk level RL to each deck officer.