• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.419-430
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• 2004

Since cDNA microarray experiments can monitor expression levels for thousands of genes simultaneously, the experimental designs and their analyzing methods are very important for successful analysis of microarray data. The loop design is discussed for selecting differentially expressed genes among several treatments and the analysis of variance method is introduced to normalize microarray data and provide estimates of the interesting quantities. MA-ANOVA is used to illustrate this method on a recently collected loop designed microarray data at Cancer Metastasis Research Center, Yonsei University.

• Journal of Veterinary Clinics
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• v.27 no.1
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• pp.71-78
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• 2010

Analysis of variance (ANOVA) is a method to analyze the data from the experimental designs comparing two or more groups or treatments at the same time, and is the most effective tool of analyzing more complex data sets with different source of variations. This article describes the logic of ANOVA, the application of the method to the analysis of a simple data set, and the methods available for performing planned or post hoc multiple comparisons between the treatments means. In addition, the common misuse of the techniques is also discussed to emphasize that an inappropriate statistical analysis is potentially far more harmful than poorly conducted research. Lastly, an example is given for illustration purposes.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.145-151
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• 2011

This study assumed the competitive structure based on genre classification for Korean online game contents market, and carried out the analysis on the degree and characteristics of the competition that appear differently in each sub market classified according to the genre. First, to analyze the market power of the rank 1 and 2 game contents in each genre, using the play time share ratio and standard deviation statistics values in the genre, ANOVA analysis and Cluster analysis were carried out for each genre. According to ANOVA analysis result, in the rank 1 game share ratio in each genre, there was a relationship of 'FPS/Racing > RST/Sports > Poker > Go-stop > RPG > Arcade > Board', and in the play time total share ratio of rank 1 and 2 games, the relationship of 'RTS > FPS/Racing > Sports > RPG > Go-stop > Poker > Arcade > Board' was verified. And in Cluster analysis, the groups of the genres with the degree of market power tendency and the variability at similar level were classified and stated.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.579-591
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• 2022

Functional data is collected in various fields. It is often necessary to test whether there are differences among groups of functional data. In this case, it is not appropriate to explain using the point-wise ANOVA method, and we should present not the point-wise result but the integrated result. Various studies on functional data analysis of variance have been proposed, and recently implemented those methods in the package fdANOVA of R. In this paper, I first explain ANOVA and multivariate ANOVA, then I will introduce various methods of analysis of variance for univariate and multivariate functional data recently proposed. I also describe how to use the R package fdANOVA. This package is used to test equality of weekly temperatures in Seoul and Busan through univariate functional data ANOVA, and to test equality of multivariate functional data corresponding to handwritten images using multivariate function data ANOVA.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.11
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• pp.69-75
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• 2019

Oil seals have a great effect on transmission performance and durability. In this study, the optimal rubber mix was derived using dispersion analysis to obtain excellent oil-seal rubber properties. ANOVA was performed twice. The factors were polymers, carbon, magnesium oxide, and calcium hydroxide, which were used as four factors in ANOVA. The response factors were four items (hardness, tensile strength, elongation rate, and compression deformation) obtained through an experiment with a confidence level of 95%. In the first ANOVA, 168 tests were performed, and in the secondary ANOVA, 24 physical tests were conducted using polymers and carbon derived from the primary ANOVA. Through the ANOVA, we derived a rubber mixture recipe.

• Proceedings of the Safety Management and Science Conference
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• 2010.11a
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• pp.539-550
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• 2010

The study presents the application strategy and understanding of regression analysis with GLM(Generalized Linear Model) unifying with other statistical techniques such as correlation analysis and design of experiment(DOE). The quidelines proposed in this paper can be used for practioners to implement GLM and ANOVA(Analysis of Variance) for the DMAIC 5 steps of six sigma breakthrough.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1409-1419
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• 2013

We analyzed the data from KYPS using the latent growth model which has been widely studied as an analysis method of longitudinal data. In this study, we applied repeated measures ANOVA to unconditional model in order for faster decision of the unconditional model of the latent growth model. Also, we compared the six-type models, the quadratic model and the model of which repeated measures ANOVA is applied.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 1998.10b
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• pp.50-51
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• 1998

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.975-982
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• 2005

The purpose of the Gage R&R study is to determine whether a measurement system is adequate for monitoring a process. If the measurement system variation is small relative to the process variation, then the measurement system is deemed 'adequate'. The sources of variation associated with the measurement system are compared using an analysis of variance (ANOVA) model, in general. A typical ANOVA model used in a standard Gage R&R study is the two-factor random effect model. Then, the ANOVA partitions the total variation into three categories: repeatability, reproducibility, part variation. However, if the process variation possesses the between group variation, within group variation, and within-part variation, these variations can cause the measurement system evaluation to provide misleading results. That is, in the standard Gage R&R study these variations affect the estimate of repeatability, reproducibility, or both. This paper presents a four-factor nested factorial ANOVA model which explicitly considers these variations for the Gage R&R study. The variance component estimates are derived by setting the EMS equations equal to the corresponding mean square from the ANOVA table and solving. And the proposed model is compared with the standard Gage R&R model.

• IE interfaces
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• v.18 no.4
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• pp.444-453
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• 2005

The purpose of the Gage R&R study is to determine whether a measurement system is adequate for monitoring a process. If the measurement system variation is small relative to the process variation, then the measurement system is deemed "adequate". The sources of variation associated with the measurement system are compared using an analysis of variance (ANOVA) model, in general. A typical ANOVA model used in a standard Gage R&R study is the two-factor random effect model. Then, the ANOVA partitions the total variation into three categories: repeatability, reproducibility, part variation. However, if the process variation possesses the between group variation, within group variation, and within part variation, these variations can cause the measurement system evaluation to provide misleading results. That is, in the standard Gage R&R study these variations affect the estimate of repeatability, reproducibility, or both. This paper presents a four-factor nested factorial ANOVA model which explicitly considers these variations for the Gage R&R study. The variance component estimators are derived by setting the EMS equations equal to the corresponding mean square from the ANOVA table and solving. And the proposed model is compared with the standard Gage R&R model.