On regression adjustments in experiments with several treatments
The Annals of Applied Statistics · 2008 · Volume 2 · Issue 1
Abstract
Regression adjustments are often made to experimental data. Since randomization does not justify the models, bias is likely; nor are the usual variance calculations to be trusted. Here, we evaluate regression adjustments using Neyman’s nonparametric model. Previous results are generalized, and more intuitive proofs are given. A bias term is isolated, and conditions are given for unbiased estimation in finite samples.
Keywords
balanced designs, bias, estimation, experiments, intention-to-treat, models, multiple regression, Randomization
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