• Journal of the Korean Statistical Society
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• v.11 no.1
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• pp.1-11
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• 1982

Srivastava and Anderson(1970) illustrate a method of obtaining Balanced (but not orthogonal) Resolution $IV^*$ designs starting with a BIB design. The incidence matrix of a BIB design with parameters (v, b, r, k, and $\lambda$) is utilized to obtain Balanced Resolution $IV^*$ designs with m factors and n=2b runs, where $m \leq v$. In this paper, the same idea is extended to the case of PBIB designs to obtain Partially Balanced Resolution $IV^*$ designs. In the designs obtained here the variances are balanced and the covariances are partially balanced with respect to the main effects. A proof of this property of Partially Balanced Resoultion $IV^*$ designs is given. The efficiency of Partially Balanced Resolution $IV^*$ designs is also considered and examples of Partially Balanced Resoultion $IV^*$ designs are included.

• Journal of the Korea Safety Management & Science
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• v.7 no.5
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• pp.69-84
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• 2005

This paper proposes statistically designed experiments which provide a proactive means to implement safety and environmental applications. Minimal experimental designs such as fractional factorial design, Plackett-Burman design, Box-Behnken design are economical and can be achieved tremendous savings with relatively few experiments. These experimental designs and analysis methods are illustrated with cases.

• Journal of the Korean Statistical Society
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• v.34 no.2
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• pp.141-151
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• 2005

Under the assumption that the three-level factors are quantitative, the linear effects are taken more attention than the quadratic effects of the interaction terms. Webb (1971) presented some small incomplete factorial designs that are mixed two- and three-level designs with 20 or fewer runs. The designs provided the estimating linear-by-linear components of interactions between the three-level factors; moreover, they could also offer estimation of interactions that interest the experiments. Webb used ad hoc methods to find these plans; hence, there was still no unified structure to those experiments. In this paper, we develop the methods to construct the $2^{n}3^3$ and $2^{1}3^3$ designs. The designs constructed by these methods not only supply orthogonal estimates of all the main effects but also permit estimation of all the two-factor interactions not involving the quadratic effects. Furthermore, the designs we find are nearly orthogonal.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.E2
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• pp.83-91
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• 2008

This work investigated the methodology to simultaneously optimize the ozone and relative humidity composition for the $NO_x$ degradation using soil biofilter. Experiments were made as a function of inlet ozone concentration ($0{\sim}1,770\;ppb$) and relative humidity ($38{\sim}81%$). Factorial design ($2^2+3$) and response surface methodology by central composite designs were used to examine the role of two factors and optimal response condition on $NO_x$ degradation. It was found that a second-order response surface model can properly interpret the experimental data with an $R^2$-value of 0.9730 and F-value of 71.83, based on which the maximum $NO_x$ degradation was predicted up to 92.8% within our experimental conditions.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.121-129
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• 2006

Two color or cDNA microarrays are extensively used to study relative expression levels of thousands of genes simultaneously. 0かy two tissue samples can be hybridized on a single microarray slide. Thus, a microarray slide necessarily forms an incomplete block design with block size two when more than two tissue samples are under study. We also need to control for variability in gene expression values due to the two dyes. Thus, red and green dyes form the second blocking factor in addition to slides. General design problem for these microarray experiments is discussed in this paper. Designs for factorial cDNA microarrays are also discussed.

• Journal of the Korean Statistical Society
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• v.3 no.2
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• pp.85-101
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• 1974

In a Balanced Factorial Experiments (BFE) with n factors $F_1, F_2,\cdots,F_n$ at $m_1, m_2,\cdots,m_n$ levels respectively, Shah [15] has considered the following association scheme: the two treatments are the $(P_1, P_2,\cdot,P_n)$th associates, where $p_i=1$ if the ith factor occurs at the same level in both treatments and $p_i=0$ otherwise; $\lambda_{(p_1,p_2,\cdots,p_n)}$ will denote the number of times these treatments occur together in a block. He has showed that a BFE is partially Blanced Incomplete Block(PBIB) design with repsect to the above association scheme. Kurjian and Zelan [6] have proved that factorial designs possessing a Property A (a particular structure for their matrix NN') are factorially balanced.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1053-1065
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• 2006

This paper proposes a method in one-way layouts for longitudinal data with informative drop-out. When dropouts are informative, that is, correlated with unobserved data and/or the previous observed data, the simple imputation methods such as 'last observation carried forward' (LOCF) methods would arise the bias of the testing models. The maximum likelihood procedure combined with a logit model for the drop-out process is proposed to test treatment effects for one factorial designs and compared with LOCF method in two examples.

• Journal of the Korean Home Economics Association
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• v.23 no.3
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• pp.49-53
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• 1985

The typical texture profile analysis of acorn flour gel was investigated with Instron univ. testing machine by two experimental designs, 3$\times$3 latin square and $3^{3}$factorial experiment. As the result, it was revealed that Latin square is a useful method to reduce the number of experiments, in the case of a negligible interaction.

• Food Engineering Progress
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• v.15 no.2
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• pp.97-115
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• 2011

A review about the application of response surface methodology in the optimization of food technology is presented. The theoretical principles of response surface methodology and steps for its application are described. The response surface methodologies : three-level full factorial, central composite, Box-Behnken, and Doehlert designs are compared in terms of characteristics and efficiency. Furthermore, recent references of their uses in food technology are presented. A comparison between the response surface designs (three-level full factorial, central composite, Box-Behnken and Doehlert design) has demonstrated that the Box-Behnken and Doehlert designs are slightly more efficient than the central composite design but much more efficient than the three-level full factorial designs.

• Journal of the Korean Statistical Society
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• v.31 no.3
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• pp.277-299
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• 2002

Most statistical designs, such as orthogonal designs and optimal designs, are based on a specific statistical model. It is very often that the experimenter does not completely know the underlying model between the response and the factors. In computer experiments, the underlying model is known, but too complicated. In this case we can treat the model as a black box, or model to be unknown. Both cases need a space filling design. The uniform design is one of space filling designs and seeks experimental points to be uniformly scattered on the domain. The uniform design can be used for computer experiments and also for industrial experiments when the underlying model is unknown. In this paper we shall introduce the theory and method of the uniform design and related data analysis and modelling methods. Applications of the uniform design to industry and other areas are discussed.