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8.4 Practical 111variety 1 2 3 4 5 6 712.836 15.490 14.209 14.519 15.333 15.274 14.007variety 8 9 10 11 12 13 1414.574 12.989 11.932 13.272 14.838 16.190 13.266variety 15 16 17 18 19 20 2114.980 13.461 14.982 15.922 16.696 16.399 14.934variety 22 23 24 2516.444 13.291 15.465 16.306Standard errors of differences of combined meansTable varietyrep. 6s.e.d. 0.6202effective d.f. 79.99Estimated stratum variancesVariate: yieldStratum variance effective d.f. variance componentreplicates 26.6655 5.000 0.4262replicates.rows 8.6037 23.464 1.5595replicates.columns 8.2120 23.438 1.4812replicates.rows.columns 0.8062 73.099 0.8062The example reinforces the point that the REML output is the same as that given by ANOVAwhen both are feasible, but that the generality of the REML method leaves aspects that itcannot duplicate. More importantly, though, it shows that the REML method makes useof all the available information about each fixed effect. These aspects indicate theefficiency and appropriateness of the methodology, and the exercises at the end of thechapter will illustrate its ability to handle designs that cannot be analysed by ANOVA.Another important advantage is that REML can fit models to spatial correlation structures.Details are given in the Guide to the GenStat Command Language, Part 2, Section 5.4,and the Guide to REML in GenStat, Chapters 3 and 4.8.4 PracticalGenStat spreadsheet file Vartrial1.gsh contains data from a trial of 35 varieties ofwheat. The design has two replicates each laid out in a five by seven plot array. Assumingthat the same block structure is appropriate as in Section 8.3 (rows crossed with columnswithin replicates), analyse the data as a linear mixed model.