• International Journal of Reliability and Applications
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• v.14 no.2
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• pp.107-114
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• 2013

Modelling of failures is an important element of reliability modelling. Empirical modelling approach suitable for complex item is explored in this paper. First step of the empirical modelling approach is to plot hazard function, density function, Weibull probability plot as well as cumulative intensity function to see which model fits best for the given data. Next step of the empirical modelling approach is select appropriate model for the data and fit the parametric model accordingly and estimate the parameters.

• Journal of Korea Water Resources Association
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• v.47 no.2
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• pp.161-170
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• 2014

It is important to estimate an appropriate quantile for design of hydraulic structure. For this purpose, it is necessary to find the appropriate probability distribution which can represent the sample data well. Probability plot correlation coefficient test as one of goodness-of-fit test, is recently developed and has been known as a simple and powerful method. In this study, probability plot correlation coefficient test statistics using the plotting position considering the coefficients of skewness for the GEV distribution is derived, and represented by the regression equation. Monte-Carlo method is also performed to compare the rejection power between each method. As the results, the probability plot correlation coefficient test which is derived in this study is better than the others. In particular, when sample size is small and distribution has the shape parameter, rejection power of probability plot correlation coefficient test considering the coefficients of skewness is bigger than the others.

• The KIPS Transactions:PartD
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• v.10D no.5
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• pp.829-836
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• 2003

Software Reliability Growth Model that have been studied variously. But measurement of correct parameter of this model is not easy. Specially, estimation of correct model about failure data must be establish and estimation of parameter can consist exactly. To get correct testing, we calculate the normal score and describe the normal probability plot. Use the normal probability plot, we estimate the distribution for failure data. In this paper, we estimate the software reliability growth model for through the normal probability plot. In this research, we applies software reliability growth model through distribution characteristics of failure data. If we see plot, we determine the software reliability growth model, we can make sure superior in model's performance estimation.

• Journal of Korean Society for Quality Management
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• v.34 no.1
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• pp.27-33
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• 2006

In this paper, an effective method for identifying influential main effects and interactions in the 2-level factorial designs is suggested by exploiting the resolution V designs developed by Kim(1992). For analysis of such designs, we employ the Bayesian approach for easy and clear identification of influential effects in the half normal probability plot.

• Journal of Korean Society of Water and Wastewater
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• v.28 no.3
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• pp.345-358
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• 2014

For introducing the groundwater quality assessment using background concentration of groundwater, several methods had been studied to estimate the background concentration of groundwater and to suggest the background concentration of study area. Some methods such as Box whisker plot, Percentile and Cumulative probability distribution had been adopted to estimate background concentration, and it was evaluated that the Cumulative probability distribution method presents more reasonable background concentration because it can consider the data distribution. So we estimated the background concentration of study area using cumulative probability distribution method. We suggested the background concentration for each hydrogeology respectively in case hydrogeological water quality similarity is very low.

• Journal of Environmental Science International
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• v.17 no.1
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• pp.57-66
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• 2008

The frequency analysis and the probability plot were applied to heavy metal contents of soils collected from the Goseong Cu mine area as a statistic method for the determination of the threshold value which was able to partition a population comprising largely dispersed heavy metal contents into the background and the anomalous populations. Almost all the heavy metal contents of soil showed a positively skewed distributions and their cumulative percentage frequencies plotted as a curved lines on logarithmic probability plot which represent a mixture of two or more overlapping populations. Total Cu, Pb and Cd data and extractable Cu and Pb data could be partitioned into background and anomalous populations by using the inflection in each curve. The others showed a normally distributed population or an largely overlapped populations. The threshold values obtained from replotted frequency distributions with the partitioned populations were Cu 400 mg/kg, Pb 450 mg/kg and Cd 3.5 mg/kg in total contents and Cu 40 mg/kg and Pb 12 mg/kg in extractable contents, respectively. The thresholds for total contents are much higher than the tolerable level of soil pollution proposed by Kloke(Cu 100 mg/kg, Pb 100 mg/kg, Cd 3 mg/kg), but those for extractable contents are not exceeded the worrying level of soil pollution proposed by Ministry of Environment(Cu 50 mg/kg, Pb 100 mg/kg). When the threshold values were used as the criteria of soil pollution in the study area, $9{\sim}19%$ of investigated soil population was in polluted level. The spatial distributions of heavy metal contents greater than threshold values showed that polluted soils with heavy metals are restricted within the mountain soils in the vicinity of abandoned mines.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.1-9
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• 2007

This investigation on the change of the daily maxima temperature in Seoul, Daegu, Chunchen, Youngchen was triggered by news items such as the earth is getting warmer and a recent news item that said that Korea is getting warmer due to this climatic change. A statistical analysis on the daily maxima for June over this period in Seoul revealed a positive trend of 1.1190 centigrade over the 45 years, a change of 0.0249 degrees annually. Due to the large variation on these maximum temperatures, one can raise the question on the significance of this increase. To check the goodness of fit of the proposed extreme value model, we shown a Q-Q plot of the observed quantiles against the simulated quantiles and a probability plot. And we calculated statistics each month and a tolerance limit. This is tested through simulating a large number of similar datasets from an Extreme Value distribution which described the observed data very well. Only 0.02% of the simulated datasets showed an increase of this degrees or larger, meaning that the probability is very low for such an event to occur.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.479-480
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• 2009

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.671-672
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• 2008

• Journal of the Korean Ceramic Society
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• v.36 no.10
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• pp.1069-1074
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• 1999