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Generalized linear models for the analysis of quality‐improvement experiments
Author(s) -
Lee Y.,
Nelder J. A.
Publication year - 1998
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3315676
Subject(s) - generalized linear model , computer science , quality (philosophy) , linear model , dispersion (optics) , maximum likelihood , mathematics , data mining , statistics , machine learning , physics , optics , philosophy , epistemology
Generalized linear models provide a useful tool for analyzing data from quality‐improvement experiments. We discuss why analysis must be done for all the data, not just for summarizing quantities, and show by examples how residuals can be used for model checking. A restricted‐maximum‐likelihood‐type adjustment for the dispersion analysis is developed.