• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.415-421
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• 2002

Because most common assumption is normality in statistical analysis, testing normality is very important. We propose a new plot and test statistic to test for normality based on the modified Lorenz curve that is proved to be a powerful tool to measure the income inequality within a population of income receivers. We also compare the proposed test statistics with the W test (Shapiro and Wilk (1965)), TL test (Kang and Cho (1999)) in terms of the power of test through by Monte Carlo method. The proposed test is more usually powerful than the other tests except some case.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.883-891
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• 2011

For survival data we sometimes want to test a log normality hypothesis that can be changed into normality by transforming the survival data. Hence the Shapiro-Wilk type statistic for normality is generalized to randomly censored data based on the Kaplan-Meier product limit estimate of the distribution function. Koziol and Green (1976) derived Cram$\acute{e}$r-von Mises statistic's randomly censored version under the simpl hypothesis. These two test statistics are compared through a simulation study. As for the distribution of censoring variables, we consider Koziol and Green (1976)'s model and other similar models. Through the simulation results, we can see that the power of the proposed statistic is higher than that of Koziol-Green statistic and that the proportion of the censored observations (rather than the distribution of censoring variables) has a strong influence on the power of the proposed statistic.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.967-976
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• 2011

The Box-Cox transformation is a well known family of power transformations that brings a set of data into agreement with the normality assumption of the residuals and hence the response variable of a postulated model in regression analysis. Normalization (studentization) of the regressors is a common practice in analyzing microarray data. Here, we implement Box-Cox transformation in normalizing regressors in microarray data. Pridictabilty of the model can be improved using data transformation compared to studentization.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.159-171
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• 2001

Shapiro and Wilk (1972) developed a test for exponentiality with origin and scale unknown. The procedure consists of comparing the generalized least squares estimate of scale with the estimate of scale given by the sample variance. However the test statistic is inconsistent ; that is, the power of the test will not approach 1 as the sample size increases. Hence we give a test based on the ratio of two asymptotically efficient estimates of scale. We also have conducted a power study to compare the test procedures, using Monte Carlo samples from a wide range of alternatives. It is found that the suggested statistics have higher power for the alternatives with the coefficient of variation greater that or equal to 1.

• Journal of People, Plants, and Environment
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• v.24 no.6
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• pp.629-638
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• 2021

Background and objective: This study was conducted to develop diameter growth models for thinned Quercus glauca Thunb. (QGT) stands to inform production goals for treatment and provide the information necessary for the systematic management of this stands. Methods: This study was conducted on QGT stands, of which initial thinning was completed in 2013 to develop a treatment system. To analyze the tree growth and trait response for each thinning treatment, forestry surveys were conducted in 2014 and 2021, and a one-way analysis of variance (ANOVA) was executed. In addition, non-linear least squares regression of the PROC NLIN procedure was used to develop an optimal diameter growth model. Results: Based on growth and trait analyses, the height and height-to-diameter (H/D) ratio were not different according to treatment plot (p > .05). For the diameter of basal height (DBH), the heavy thinning (HT) treatment plot was significantly larger than the control plot (p < .05). As a result of the development of diameter growth models by treatment plot, the mean squared error (MSE) of the Gompertz polymorphic equation (control: 2.2381, light thinning: 0.8478, and heavy thinning: 0.8679) was the lowest in all treatment plots, and the Shapiro-Wilk statistic was found to follow a normal distribution (p > .95), so it was selected as an equation fit for the diameter growth model. Conclusion: The findings of this study provide basic data for the systematic management of Quercus glauca Thunb. stands. It is necessary to construct permanent sample plots (PSP) that consider stand status, location conditions, and climatic environments.

• Research in Community and Public Health Nursing
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• v.6 no.1
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• pp.5-33
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• 1995

The objective of the study are two-fold: one is to explore the relationship between environment, organizational structure, and organizational effectiveness of public health centers in Korea, and the other is to examine the validity of contingency theory for improving the organizational structure of public health care agencies, with special emphasis on public health nursing administration. Accordingly, the conceptual model of the study consisted of three different concepts: environment, organizational structure, and organizational effectiveness, which were built up from the contingency theory. Data were collected during the period from 1st of May through 30th of June, 1990. From the total of 249 health centers in the country, one hundred and five centers were sampled non proportionally, according to the geopolitical distribution. Out of 105, 73 health centers responded to mailed questionnaire. The health centers were the unit of the study, and a various statistical analysis techniques were used: Reliability analysis(Cronbach's Alpha) for 4 measurement tools; Shapiro-Wilk statistic for normality test of measured scores of 6 variables: ANOVA, Pearson Correlaion analysis, regressional analysis, and canonical correlation analysis for the test of the relationships and differences between the variables. The results were. as follows : 1. No significant differences between forma lization, decision-making authority and environmental complexity were found(F=1.383, P=.24 ; F=.801, P=.37). 2. Negative relationships between formalization and decision-making authority for both urban and rural health centers were found(r=-.470, P=.002 ; r=-.348, P=.46). 3. No significant relationship between formalization and job satisfaction for both urban and rural health centers were found (r=-.242, P=.132, r=-.060, P=.739). 4. Significant positive relationship between decision - making authority and job satisfaction were found in urban health centers (r=.504, P=.0009), but no such relationship was observed in rural health centers. Regression coefficient between them was statistically significant($\beta=1.535$, P=.0002), and accuracy of regression line was accepted (W=.975, P= .420). 5. No significant relationships among formalization and family planning services, maternal health services, and tuberculosis control services for both urban and rural health centers were found. 6. Among decision-making authority and family planning services, maternal health services, and tuberculosis control services, significant positive relationship was found between de cision-making authority and family planning services(r=.286, P=.73). 7. A significant difference was found in maternal health services by the type of health centers (F=5.13, P=.026) but no difference was found in tuberculosis control services by the type of health centers, formalization, and decision-making authority. 8. A significant positive relationships were found between family planning services and maternal health services and tuberculosis control services, and between maternal health services and tuberculosis control services (r=-.499, P=.001 ; r=.457, P=.004 ; r=.495, P=.002) in case of urban health centers. In case of rural health centers, relationships between family planning services and tuberculosis control services, and between maternal health services and tuberculosis control services were statistically significant (r=.534, P=.002 ; r=.389, P=.027). No significant relationship was found between family planning and maternal health services. 9. A significant positive canonical correlation was found between the group of independent variables consisted of formalization and de cision-making authority and the group of dependent variables consisted of family planning services, maternal health services and tuberculosis control services(Rc=.455, P=.02). In case of urban health centers, no significant canonical correlation was found between them, but significant canoncial correlation was found in rural health centers(Rc=.578, P=.069), 10. Relationships between job satisfaction and health care productivity was not found significant. Through these results, the assumed relationship between environment and organizational structure was not supported in health centers. Therefore, the relationship between the organizational effectiveness and the congruence between environment and organizational structure that contingency theory proposes to exist was not able to be tested. However decision-making authority was found as an important variable of organizational structure affecting family planning services and job satisfaction in urban health centers. Thus it was suggested that decentralized decision making among health professionals would be a valuable strategy for improvement of organizational effectiveness in public health centers. It is also recommended that further studies to test contingency theory would use variability and uncertainty to define environment of public health centers instead of complexity.