Titlebook: Analysis of Variance in Experimental Design; Harold R. Lindman Textbook 1992 Springer-Verlag New York, Inc. 1992 Factor.Matrix.SAS.Statist

certitude

General Linear Model, a vector of . unknown parameters, . is an . × . matrix of coefficients, and . is a vector of . random errors. (In this chapter . is a scalar representing the total number of scores; . represents the number of scores in each group.)

保守

Textbook 1992es enough theory to enable the reader to apply the methods intelligently rather than mechanically. Comprehensive, and covering the important techniques in the field, including new methods of post hoc testing. The relationships between different research designs are emphasized, and these relationship

表脸

https://doi.org/10.1007/978-3-322-94971-4e not planned, but were suggested by the data. Both kinds of techniques are useful. In a well-planned experiment there are often specific differences in which we are interested; however, we should also be aware of unexpected differences in the data.

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W. W. Buchanan,P. J. Rooney,G. Kraag those in Figure 10.1 (taken from the data in Table 10.1); in this graph the numerical values of the factor levels dictate both their order and their spacing along the . axis. By contrast, for the data plotted in Figure 3.1, both the ordering and the spacing of the factor levels were arbitrary.

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闪光东本

Comparing Groups,e not planned, but were suggested by the data. Both kinds of techniques are useful. In a well-planned experiment there are often specific differences in which we are interested; however, we should also be aware of unexpected differences in the data.

mettlesome

One-Way Designs with Quantitative Factors, those in Figure 10.1 (taken from the data in Table 10.1); in this graph the numerical values of the factor levels dictate both their order and their spacing along the . axis. By contrast, for the data plotted in Figure 3.1, both the ordering and the spacing of the factor levels were arbitrary.

附录

Analysis of Covariance,riance, as compared with other possible ways of solving the same problem. Finally, we will describe the general analysis of covariance, with examples. The reader who wants only a general understanding of analysis of covariance can skip the final section.

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https://doi.org/10.1007/978-3-319-65175-0rugs. In some cases, however, the groups or “treatments” themselves may have been selected randomly from a large number of potential treatments. In this chapter we will consider methods for analyzing such data.

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