Analysis of Means for Analyzing Missing Data from Experimental Designs – Part II
Keywords:
Analysis of Means, Missing Data, Replicated Latin Square Designs, Cross Over Designs, F-Square DesignsAbstract
A step-by-step analysis of means (ANOM) procedure proposed by Subramani (1992) to analyze the missing data from randomized block designs has been extended to other experimental designs with missing observations. In part I of this paper, we have applied this method for analyzing the missing data from latin square designs, graeco-latin square designs and hyper graeco -latin square designs. In part II of this paper, it is decided to analyze the missing data from replicated latin square designs of Type I , Type II and Type III, cross over designs and F-Square designs. The procedure is also illustrated with the help of numerical examples.
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References
COCHRAN, W.G. and COX. G.M. (1957): Experimental Designs, 2nd Edition, Wiley, New York.
HEDAYAT, A. and SEIDEN, E. (1970): F-Square and Orthogonal F-Squares Design: A Generalization of Latin Square and Orthogonal Latin Squares Design., Ann. Math. Statist. 41, 2035-2044.
HEDAYAT, A., RAGHAVA RAO, D. and SEIDEN, E. (1975): Further Contributions to the theory of F-Square Design, Ann. Statist. 3, 712-716.
JOHN, J. A. and QUENOUILLE, M. H. (1977): Experiments: Design and Analysis, Charles Griffin, London, UK.
MONTGOMERY, D.C. (1984): Design and Analysis of Experiments, 2nd Edition, Wiley, New York.
OTT. E.R.(1967): Analysis of Means – A Graphical Procedure, Industrial Quality Control, 24, 101-109.
SCHILLING, E.G. (1973): A Systematic Approach to Analysis of Means, Journal Of Qaulity Technology, 5, 92-108,147-159
SUBRAMANI, J. (1991): Non-iterative Least Squares Estimation of Missing Values in Graeco-Latin Square Designs, Biometrical Journal, 33, 763-769.
SUBRAMANI, J (1991): Non-iterative Least Squares Estimation of Missing Values In Replicated Latin Square Designs, Biometrical Journal, 33, 999-1011.
SUBRAMANI, J. (1992): Analysis of Means for Experimental Designs with Missing Observations, Communi. in Statistics – Theory and Methods, 21, 2045-2057.
SUBRAMANI, J (1993): Non-iterative Least Squares Estimation of Missing Values In Hyper-Graeco-Latin Square Designs, Biometrical Journal, 35, 465-470.
SUBRAMANI, J. (1994): Non-iterative Least Squares Estimation of Missing Values In Cross-Over Designs without Residual Effect, Biometrical Journal, 36,285-292.
SUBRAMANI, J. (2008a): Analysis of Means for Analysing Missing Data from Experimental Designs- Part I – Journal of Manufacturing Engineering, 3, 136-145.
SUBRAMANI J (2008b): Estimation of Several Missing values in Experimental Designs, South East Asian Journal of Mathematical Sciences,
SUBRAMANI, J.and AGGARWAL, M.L. (1993): Estimation of several Missing values in F-Square Designs, Biometrical Journal, 35,455-463.
SUBRAMANI, J.and PONNUSWAMY, K.N. (1989): A Non-iterative Least Squares Estimation of Missing Values in Experimental Designs, Journal Of Applied Statistics, 16, 77-86.
WILKINSON, G.N. (1958): Estimation of Missing Values for the Analysis of Incomplete Data, Biometrics, 14,257-286.
YATES, F. (1933): The Analysis of Replicated Experiments when the Field Results are Incomplete, Empire Journal of Experimental Agriculture, 129-142.