• Proceedings of the Korean Society for Quality Management Conference
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• 2006.11a
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• pp.221-226
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• 2006

We usually use two techniques(influence function and local influence) for detecting outliers. But, we cannot use these difficult techniques in elementary industrial statistics course for college students. We can use some simple graphical methods(box plot, dandelion seed plot, influence graph and cumulative deletion plot) for univariate and bivariate outlier detection and outlier effect in elementary industrial statistics course for college students.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.508-510
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• 2005

Plot Unit는 이야기를 형성하는 줄거리 또는 줄거리에 나오는 여러 사건을 하나로 구성하여 표현한다. 글을 읽고 Plot Unit를 파악한다는 것은 그 글의 내용을 이해하고 있다는 것이다. 본 논문에서는 이러한 Plot Unit의 정서상태 선호도를 결정하는 방법으로 범주 재분류를 생각하였다. Roget 범주들을 양, 음 기준에 따라서 양범주, 음범주, 중성범주로 재분류하였다. 또한, 개연규칙과 Plot Unit의 대응에 이 결과를 적용해 봄으로써, 범주 재분류를 활용하여 Plot Unit의 사건유형을 결정할 수 있음을 확인하였다.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.645-658
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• 1999

One of the most popular graphical techniques for goodness of fit problems is the quantile-quantile plot(Q-Q plot) Easton and McCulloch(1990) suggested a way of generalizing Q-Q plots to multivariate cases bases on finding a matching between the points of the data set whose shape is being examined and a reference sample. in this paper we investigated the asymptotic behavior of the generalized Q-Q plot for bivariate cases. As a result we concluded that the standard univariate Q-Q plot and the generalized Q-Q plot have the same limit if two variables are independent.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.119-128
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• 1997

Added variable plot and component-plus-residual plot are very useful for studying the role of a predictor in classical regression analysis. The former is usually used to check the effect of adding a new variable to existing model. The latter has been suggested as computationally convenient substitutes for the added variable plots, however, this plot is found to be better in detecting nonlinear relationships of a new predictor. By combining these two plots dynamically, we can take advantages of two plots simultaneously. And even further, we can get some knowledge of collinearity between a new predictor and predictors already in the model, and more accurate information about the possible outliers.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.407-417
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• 1995

The h-plot is a graphical technique for displaying the structure of one population's variance-covariance matrix. This follows the mathematical algorithem of the principle component biplot based on the singular value decomposition. But it is known that the singular value decomposition is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, since the mathematical algorithm of the h-plot is equivalent to that of principal component biplot of Choi and Huh (1994), we derive the resistant h-plot.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.439-451
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• 1995

Ranked data are widely used in the area of social sciences, for instance in polls and preference surveys, in which a number of objects (or stimuli) are evaluted and ranked by a panel of judges (or subject) according to their preference. We propose a graphical method for ranked data by quantifying objects and judges. In a plot for judges, the interpoint distances can be interpreted as Spearman or Kendall distance between two rankings given by respective judges. Similarly, we also construct a plot for objects with a sensible relationship to the previous plot for judges.

• Proceedings of the Safety Management and Science Conference
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• 2011.04a
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• pp.487-493
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• 2011

The paper reviews three Split Plot Designs (SPDs) of fixed factors, and those are SPD (RCBD, RCBD), SPD (CRD, RCBD) and SBD (Split Block Design). RCBD (Randomized Complete Block Design) and CRD (Completely Randomized Design) are used to deploy whole plot and sub plot. The models explained in this study are derived from random, crossed and nested models.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.41-52
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• 2006

Because most common assumption is normality in statistical analysis, testing normality is very important. The Q-Q plot is a powerful tool to test normality with full samples in statistical package. But the plot can't test normality in type-II censored samples. This paper proposed the modified the Q-Q plot and the modified normalized sample Lorenz curve(NSLC) for normality test in the type-II censored samples. Using the two Hodgkin's disease data sets and the type-II censored samples, we picture the modified Q-Q plot and the modified normalized sample Lorenz curve.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1441-1448
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• 2008

We propose the modified quantile-quantile (Q-Q) plot using the approximate maximum likelihood estimators and the modified normalized sample Lorenz curve (NSLC) plot for the extreme value distribution based on multiply Type-II censored samples. Using two example data sets, we picture the modified Q-Q plot and the modified NSLC plot.

• Korean Journal of Soil Science and Fertilizer
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• v.34 no.6
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• pp.407-412
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• 2001

This study was conducted to find out the effect of slow-release nitrogen fertilizer, such as latex coated urea(LCU) and Meister10(MS10) on direct seeded rice in dry soil(DS). Junghwabyeo, and early maturity rice cultivar was grown on the plots which were treated with None-nitrogen. urea. LCU and MS10 plot. Growth characteristic, yield and yield components were investigated. Nitrogen uptake-efficiency and physico-chemical properties of soil before-after experiment were analyzed. Plant height and number of tillers $m^{-2}$ in LCU and MS10 plot at early grow stages were higher than those in urea plot. Plant height and number of tillers $m^{-2}$ grown on the plot of Ms10 plot were higher than those of LCU plot. The number of seedling $m^{-2}$ were no significant differences among None-N, urea, and MS10 plot in DS. Heading date and leaf color were higher with Urea than LCU and MS10 plot. Culm length in LCU and MS10 plot were longer compared with urea plot, but panicle length was similar among with Urea, LCU and MS10 plot. Number of panicles $m^{-2}$ was greater in order of MS10 > LCU > Urea plot. Yield were greater in order of MS10 > LCU > Urea plot. Nitrogen uptake and nitrogen efficiency were greater in order of MS10 > LCU > urea plot. After the experiment, total content of nitrogen in soil was not changed at all treatments, but pH, P and Si of soil were lower than those of before experiment at all treatments.