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Heterogeneity of variance in field experiments: some causes and practical implications

Published online by Cambridge University Press:  27 March 2009

P. Krajewski
P. Krajewski
Affiliation:
Rothamsted Experimental Station, Harpenden, Herts, AL5 2JQ, UK
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Summary

Data from 62 field experiments on various crops in 1973–85 at Rothamsted Experimental Station were used to obtain knowledge about the heterogeneity of variance of yield. The results suggest that explaining heterogeneity of variance in terms of a mean–variance relationship is inadequate; better understanding of the patterns of changing variance can be gained by investigating the influence of the experimental factors on the variance. The effects of the factor ‘amount of nitrogen’ on mean yield and on the variance were positively correlated; factors which tend to affect variability but not the mean were identified. Suggestions are given for controlling variance but the need for further experimental evidence is emphasized.

Type
Crops and Soils
Information
The Journal of Agricultural Science , Volume 115 , Issue 1 , August 1990 , pp. 83 - 93
Copyright
Copyright © Cambridge University Press 1990

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References

REFERENCES

Cochran, W. G. (1938). Some difficulties in the statistical analysis of replicated experiments. Empire Journal of Experimental Agriculture 6, 157–175.Google Scholar
Cochran, W. G. (1947). Some conseque1ces when the assumptions for the analysis of variance are not satisfied. Biometrics 6, 22–38.Google Scholar
Cook, R. D. & Weisberg, S. (1983). Diagnostics for heteroscedasticity in regression. Biometrika 70, 1–10.Google Scholar
Eisenhart, C. (1947). The assumptions underlying the analysis of variance Biometrics 3, 1–21.Google Scholar
Gomez, K. A. & Gomez, A. A. (1984). Statistical Procedures for Agricultural Research, 2nd edn.New York: Wiley.Google Scholar
Hartley, H. O. & Jayatillake, K. S. E. (1973). Estimation for linear models with unequal variances. Journal of the American Statistical Association 68, 189–192.Google Scholar
Irwin, J. O. (1931). Mathematical theorems involved in the analysis of variance. Journal of the Royal Statistical Society 94, 284–300.Google Scholar
Kackar, R., Nair, V., Phadke, M., Shoemaker, A., Box, G. & Wu, C. F. J. (1987). On quality practice in Japan. Statistical Research Report, AT&T Bell Laboratories No. 45.Google Scholar
Little, T. M. & Hills, F. J. (1978). Agricultural Experimentation. Design and Analysis. New York: Wiley.Google Scholar
Nair, V. & Pregibon, D. (1986). A data analysis strategy for quality engineering experiments. Statistical Research Report, AT&T Bell Laboratories No. 38.Google Scholar
Rayner, A. A. (1967). A First Course in Biometry for Agricultural Students. University of Natal Press.Google Scholar
Weisberg, S. (1985). Applied Linear Regression. New York: Wiley.Google Scholar