Abstract
In many experiments, for each experimental unit, we may have observations on one or more supplementary variables in addition to the yield. These variables are called concomitant variables. If the concomitant variables are unrelated to treatments and influence the yield, the variation in yield caused by them should be eliminated before comparing treatments. A technique of analysis which eliminates the variation in yield due to these concomitant variables is known as Analysis of Covariance. Let us look at some examples.
I think it’s much more interesting to live not knowing than to have answers which might be wrong
— R. P. Feynman
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Madhyastha, N.R.M., Ravi, S., Praveena, A.S. (2020). Analysis of Covariance. In: A First Course in Linear Models and Design of Experiments. Springer, Singapore. https://doi.org/10.1007/978-981-15-8659-0_6
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DOI: https://doi.org/10.1007/978-981-15-8659-0_6
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