An R Companion to Linear Statistical Models
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This work was prepared to serve as an R supplement for textbooks on Linear Statistical Models. It provides computational and coding details on the use of R that textbooks do not. Topics covered include simple and multiple linear regression models, models for one- and two-factor fixed-effects designs, covariance models, and models for randomized complete block designs. The text can serve as both a course supplement and a fairly detailed self-help resource. The development of "grass-roots" code alongside demonstrations of pre-packaged routines provides users with illustrations on how to develop ...
This work was prepared to serve as an R supplement for textbooks on Linear Statistical Models. It provides computational and coding details on the use of R that textbooks do not. Topics covered include simple and multiple linear regression models, models for one- and two-factor fixed-effects designs, covariance models, and models for randomized complete block designs. The text can serve as both a course supplement and a fairly detailed self-help resource. The development of "grass-roots" code alongside demonstrations of pre-packaged routines provides users with illustrations on how to develop their own programs with R.
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