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Author: search.filters.author.Leerojanaprapa, KanogkanSubject: search.filters.subject.Design of experiment

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    Item type:Publication,
    Full-model regression sum of squares of randomized complete block design having one unrecorded observation
    (2019-01-01)
    Sirikasemsuk, Kittiwat
    ;
    Leerojanaprapa, Kanogkan
    ;
    Sirikasemsuk, Sirisak
    In the classical design of experiment, a randomized complete block design (RCBD) is a helpful experimental design because this design uses a small number of experimental units. The randomized complete block design is comprised of two factors, i.e., a nuisance factor and a potential factor. In many real experiments, some data might be missing or unrecorded. The instant formulae were not provided for an analysis of variance in this case. This paper took into account the randomized complete block design (RCBD) with a treatments and b blocks. In this contribution, the RCBD with an unrecorded value was analyzed by means of the exact scheme with the model comparison approach. The advantages of the exact scheme are that the unrecorded value for the unfilled cell is not estimated and the treatment sum of squares is unbiased. It is important to note that there is no ready-made formula for RCBD in the past. Hence, this research paper provided the mathematical formulae for the fitted parameters and the overall regression sum of squares (ORSS) for the full model of experimental data. It is also noted that the ORSS is imperative for the analysis of the variance by means of the exact scheme.
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    Analysis of two-missing-observation 4 × 4 latin squares using the exact approach
    (2018-01-01)
    Sirikasemsuk, Kittiwat
    ;
    Leerojanaprapa, Kanogkan
    This research deals with the analysis of incomplete Latin square designs using the exact approach. Specifically, the study investigated the 4 × 4 Latin square designs with two missing observations without replication. In the research, the general regression significance test (i.e. the exact approach) was used to derive the estimation formulae of fitted parameters for the full and reduced linear statistical models, thereby simplifying the calculation process. In addition, the proposed exact approach-based formulae facilitate the determination of the treatment sum of squares and the error sum of squares, both of which are subsequently employed in the analysis of variance (ANOVA).