# A Comparative Analysis of Wheat Yield Using the Range-Control Analysis and the Latin- Square Design

### Abstract

Having looked at the use of Latin-Square Design in analyzing agricultural data, it is viewed that a relatively simple alternative can come from statistical quality control. The data set considered in this paper is for wheat yield resulting from a Latin-Square Design. Statistical quality control is basically meant for analyzing data on manufactured products. The data- presentation format for both wheat yield and manufacture data can be the same if the researcher so chooses. One of the possibilities is the data- presentation format for range- control analysis [where the sample size ,n, can be made equal to theÂ number of samples, m]Â and that of the Latin -Square Design where the number of rows is equal to the number of columns is equal to the number of treatments. It is possible to make all these variables equalÂ for the two methods, and this is what has been done in this paper. The other imposed condition is that if the sample means are significantly different, then, theyÂ are deemed collectively effective. Range-control analysis is a relatively simple method among the methods used in statistical qualityÂ Â control to determine whether or not the manufactured items meet a pre-manufacture set standard.Â For reasons of simplicity, this quality control method has been proposed as a possible alternative to theÂ Latin-Square Design. The results of the tests conducted using the range- control analysis and the analysis of variance for the Latin â€“Square design lead to the same statistical conclusions: the effect of rows and columns on the wheat yield is not significant, but the effect of the treatments significantlyÂ influenced the wheat yield.Â Â Hence, it is concluded that the two methods used in the analysis are good alternatives.

### References

Adigun. J.A. Lagoke, S.T.O., Adekpe, I.D. 2003. “Efficacy of selected herbicides for weed control in rain-fed upland rice in the Nigerian Northern Guinea Savanna.” ASSET, Journal, University of Agriculture, Abeokuta, Nigeria, Series A, Vol.3 No.1 pp. 43-51

Alabi-Labaika, A. B. 2005. Principles and Administration of statistical quality control, University of Lagos Press, Lagos, pp. 24-30

Androulidakis, S.I., Siardos, G.C. 1996. “Agriculture Extension Agents Perceptions Regarding Their Relevance and Competence in Certain Professional Task Areas” European Journal of Agricultural Educational Extension Vol 1 No 3 pp. 1-14

Banjoko, S. A. 1989. Techniques of production management, Lagos, Development Press Ltd. Pp.255-272

Blank, L.1980. Statistical Procedures for Engineering, Management and Science, New York McGraw-Hill Book Co pp. 256-549

Brookes, B. C., Dick, W. F. L. 1969. Introduction to Statistical Methods, 2nd edition, London: Heinemann pp.264-267

Caswell, F. 1982. Success in Statistics, John Murray Publishers Ltd. Pp258-263

Moroney, M.J. 1976. Facts From Figures, London, Cox and Wyman Ltd:. Pp. 141-215 & 390-394

Neter. J., Wasserman, W., Kutner, M. H. 1990. Applied Linear Statistical Methods 3rd Edition, Boston: IRWIN, pp. 522-560

Summers, G.W., Peters, I.S., Armstrong, C.P. 1977. Basic Statistics in Business and Economics, Belmont, Wadsworth Publishing. PP. 378-385.

Moroney, M.J. 1976. Facts From Figures, London, Cox and Wyman Ltd:. Pp. 141-215 & 390-394

Neter. J., Wasserman, W., Kutner, M. H. 1990. Applied Linear Statistical Methods 3rd Edition, Boston: IRWIN, pp. 522-560

Summers, G.W. Peters, I.S., Armstrong, C.P. 1977. Basic Statistics in Business and Economics, Belmont, Wadsworth Publishing. PP. 378-385.