TY - JOUR

T1 - Sums of squares and expected mean squares in SAS

AU - Driscoll, Michael F.

AU - Borror, Connie M.

PY - 2000/9/1

Y1 - 2000/9/1

N2 - The four different types of sums of squares available in SAS are considered, and a broad overview is given of how the similarities and dissimilarities between them depend upon the structure of the data being analyzed (for example, on the presence of empty cells). The fixed-effect hypotheses tested by these sums of squares are discussed, as are the expected mean squares computed by SAS procedure GLM. Primary attention is given to linear models for the analysis of variance. Only two-factor analysis of variance models are explicitly considered, since they are complex enough to illustrate the most important points. Numerical examples are included.

AB - The four different types of sums of squares available in SAS are considered, and a broad overview is given of how the similarities and dissimilarities between them depend upon the structure of the data being analyzed (for example, on the presence of empty cells). The fixed-effect hypotheses tested by these sums of squares are discussed, as are the expected mean squares computed by SAS procedure GLM. Primary attention is given to linear models for the analysis of variance. Only two-factor analysis of variance models are explicitly considered, since they are complex enough to illustrate the most important points. Numerical examples are included.

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U2 - 10.1002/1099-1638(200009/10)16:5<423::AID-QRE351>3.0.CO;2-W

DO - 10.1002/1099-1638(200009/10)16:5<423::AID-QRE351>3.0.CO;2-W

M3 - Article

AN - SCOPUS:0034268567

VL - 16

SP - 423

EP - 433

JO - Quality and Reliability Engineering International

JF - Quality and Reliability Engineering International

SN - 0748-8017

IS - 5

ER -