English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Traditional methods for covariate adjustment of treatment means in designedexperiments are inherently conditional on the observed covariate values. Inorder to develop a coherent general methodology for analysis of covariance, wepropose a multivariate variance components model for the joint distribution ofthe response and covariates. It is shown that, if the design is orthogonal withrespect to (random) blocking factors, then appropriate adjustments to treatmentmeans can be made using the univariate variance components model obtained byconditioning on the observed covariate values. However, it is revealed thatsome widely used models are incorrectly specified, leading to biased estimatesand incorrect standard errors. The approach clarifies some issues that havebeen the source of ongoing confusion in the statistics literature.

A Multivariate Variance Components Model for Analysis of Covariance in Designed Experiments