Abstract

GONZALEZ-HUERTA, Andrés et al. Balanced subsampling in single-factor experiments using InfoStat and InfoGen: validation with SAS. Rev. Mex. Cienc. Agríc [online]. 2023, vol.14, n.2, pp.235-249.  Epub June 19, 2023. ISSN 2007-0934.  https://doi.org/10.29312/remexca.v14i2.3418.

Even today there is little published information regarding the analysis of single-factor experiments when an equal number of subsamples are used within each experimental unit. This study analyzes male flowering data recorded in four varieties of corn (Zea mays L.) established under field conditions using four repetitions per treatment, 30 data were recorded within each experimental unit, but for the present study only three of these are considered. The experimental designs selected were completely randomized, randomized complete blocks and Latin square. The outputs were obtained with InfoStat and correspond to an analysis of variance and a comparison of means of treatments with the Tukey test (p= 0.01), and these can also be generated with InfoGen applying the same procedure. The data leading to both results were used for manual calculations and the results are validated with the statistical analysis system. Because the data are the same, the sampling error is common in the three experimental designs and it is shown how to obtain the joint error, the difference between the two generates the experimental error. To simplify the procedure on the personal computer, a single database is produced. Only for the case of the Latin square design, the matrix expressions that allow homologating the manual calculation with sums of squares in the analysis of variance are provided. If the secondary objective were to compare the three experimental designs, the statistical support generated by them would allow it, in a single run using SAS and individually for each design applying InfoStat and InfoGen.

Keywords : free statistical packages; matrix algebra; sampling error in experimental designs; sum of squares in trials with subsampling.

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