• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.227-237
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• 2014

We use the regression model with the indicator variables in the case that we use qualitative variables as some predictor variables in regression analysis. We use the ANCOVA(Analysis of Covariance) model when comparing the response variable among groups while statistically controlling for variation in the response variable caused by a variation in the covariate. DD-plot can be used as a graphical exploratory data analysis tool before the confirmatory data analysis. With the DD-plot, we can discriminate the difference of groups in the regression model with the indicator variables or the ANCOVA model at a glance. Making DD-plot does not demand the statistical model assumption about error terms in regression model. Several examples show the usefulness of DD-plots as a graphical exploratory data analysis tool for the regression analysis.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.171-179
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• 2006

For the problem of variable selection in linear models, we consider the errors are correlated with V covariance matrix. Hocking's theorems on the effects of the overfitting and the underfitting in linear model are extended to the less than full rank and correlated error model, and to the ANCOVA model.

• Communications for Statistical Applications and Methods
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• v.19 no.5
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• pp.721-729
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• 2012

Various methods control the influence of a covariate on a response variable. These methods are analysis of covariance(ANCOVA), RANK ANCOVA, ANOVA of (covariate-adjusted) residuals, and Kruskal-Wallis tests on residuals. Covariate-adjusted residuals are obtained from the overall regression line fit to the entire data set that ignore the treatment levels or factors. It is demonstrated that the methods on covariate-adjusted residuals are only appropriate when the regression lines are parallel and covariate means are equal for all treatments. In this paper, we proposed the new nonparametric method on the ANCOVA model, as applying joint placement in a one-way layout on residuals as described in Chung and Kim (2007). A Monte Carlo simulation study is adapted to compare the power of the proposed procedure with those of the previous procedure.

• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.187-192
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• 2005

For the problem of variable selection in linear models, we consider the errors are correlated with V covariance matrix. Hocking's theorems on the effects of the overfitting and the undefitting in linear model are extended to the less than full rank and correlated error model, and to the ANCOVA model

• Journal of applied mathematics & informatics
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• v.32 no.3_4
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• pp.343-357
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• 2014

In many non-life insurance applications past data are given in a form known as the run-off triangle. Smoothing such data using parametric crisp regression models has long served as the basis of estimating future claim amounts and the reserves set aside to protect the insurer from future losses. In this article a fuzzy counterpart of the Hoerl curve, a well-known claim reserving regression model, is proposed to analyze the past claim data and to determine the reserves. The fuzzy Hoerl curve is more flexible and general than the one considered in the previous fuzzy literature in that it includes a categorical variable with multiple explanatory variables, which requires the development of the fuzzy analysis of covariance, or fuzzy ANCOVA. Using an actual insurance run-off claim data we show that the suggested fuzzy Hoerl curve based on the fuzzy ANCOVA gives reasonable claim reserves without stringent assumptions needed for the traditional regression approach in claim reserving.

• Communications for Statistical Applications and Methods
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• v.24 no.3
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• pp.241-254
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• 2017

Drawing a sample can be costly or time consuming in some studies. However, it may be possible to rank the sampling units according to some baseline auxiliary covariates, which are easily obtainable, and/or cost efficient. Ranked set sampling (RSS) is a method to achieve this goal. In this paper, we propose a modified approach of the RSS method to allocate units into an experimental study that compares L groups. Computer simulation estimates the empirical nominal values and the empirical power values for the test procedure of comparing L different groups using modified RSS based on the regression approach in analysis of covariance (ANCOVA) models. A comparison to simple random sampling (SRS) is made to demonstrate efficiency. The results indicate that the required sample sizes for a given precision are smaller under RSS than under SRS. The modified RSS protocol was applied to an experimental study. The experimental study was designed to obtain a better understanding of the pathways by which positive experiences (i.e., goal completion) contribute to higher levels of happiness, well-being, and life satisfaction. The use of the RSS method resulted in a cost reduction associated with smaller sample size without losing the precision of the analysis.

• Journal of Korean Home Economics Education Association
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• v.17 no.3 s.37
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• pp.109-121
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• 2005

The Purpose of this research was to find out differences in motivation and achievement by gender between traditional group and ARCS group. The subjects were 217 first graders in six different classes from a middle school in Daegu. Each Class had Five lessons during 5 weeks. The collected data were analyzed using descriptive statistics, ANOVA and ANCOVA by SPSS 10.0 program. The results of this study are as follows : First, the ARCS group showed significantly higher score than the control group in motivation. But no significant difference was found between boys and girls and in interactive effects. Statistically significant differences were found in three factors of motivation - attention, relevance and satisfaction - from the ARCS model, but no significant difference in confidence. Second, there was no significant difference in students' achievement.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.6
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• pp.765-772
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• 2017

Objective: This study examines the genetic factors influencing the phenotypes (four economic traits:oleic acid [C18:1], monounsaturated fatty acids, carcass weight, and marbling score) of Hanwoo. Methods: To enhance the accuracy of the genetic analysis, the study proposes a new statistical model that excludes environmental factors. A statistically adjusted, analysis of covariance model of environmental and genetic factors was developed, and estimated environmental effects (covariate effects of age and effects of calving farms) were excluded from the model. Results: The accuracy was compared before and after adjustment. The accuracy of the best single nucleotide polymorphism (SNP) in C18:1 increased from 60.16% to 74.26%, and that of the two-factor interaction increased from 58.69% to 87.19%. Also, superior SNPs and SNP interactions were identified using the multifactor dimensionality reduction method in Table 1 to 4. Finally, high- and low-risk genotypes were compared based on their mean scores for each trait. Conclusion: The proposed method significantly improved the analysis accuracy and identified superior gene-gene interactions and genotypes for each of the four economic traits of Hanwoo.

• Mood & Emotion
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• v.15 no.3
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• pp.123-129
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• 2017

Objective : To present effects of depression, optimism and gratitude on suicidal ideation of patients with depressive disorders. Methods : Using analyses of covariance (ANCOVA), scores on the Beck Depression Inventory (BDI), Life Orientation Test-Revised (LOT-R), and Gratitude Questionnaire (GQ-6) were compared between depressive disorder patients with higher and lower levels of suicidal ideations. A linear regression model was fitted to detect independent correlates for suicidal ideations. Results : Significantly greater level of depression and lower level of gratitude were characterized by depressive disorder patients with higher level of suicidal ideations. The fitted regression model presented that depression and gratitude were independent correlates for suicidal ideations in patients with depressive disorders. Conclusion : Our findings suggest that gratitude may be associated with lowering the level of suicidal ideation in patients with depression.

• Journal of Korean Elementary Science Education
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• v.23 no.4
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• pp.273-278
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• 2004

According to Piaget, children aged 11 are in the middle of concrete operation period and formal operation period. So, it is necessary to adopt the Learning Cycle Model (LCM) which helps students improve their cognitive development. After determining the test for the Science Concept of Matter (SCOM), the experimental group showed higher average than the comparative group in the post-test. In the sound understanding, the experimental group showed higher ratio than the comparative group. And in the ratio of imperfect, wrong understanding and no response, the experimental group was lower than the comparative group. On the questions that were needed the complicated inquiry, many students of both groups still couldn't find the fundamental cause. In forming the scientific conceptualization, there was a meaningful difference (p < .001) after post-test Analysis of Covariance (ANCOVA) with pre-test result. After determining the test for the Test Inquiry Science Process (TISP), the experimental group showed higher average than the comparative group in the post-test. In the category of basic inquiry process which is needed in concrete operation, there was a meaningful difference (p < .05). In the category of unified inquiry process which is needed in formal operation, they showed no meaningful difference (p > .05). Therefore, applying the LCM to the chapter of 'Solution and Dissolving' is more effective on improving the scientific conceptualization and on helping the concrete operation abilities than the teacher centered learning.