User Guide

2
Chapter 1
(interaction plots) of these means allow you to visualize some of the relationships
easily. The post hoc multiple comparison tests are performed for each dependent
variable separately.
Residuals, predicted values, Cook’s distance, and leverage values can be saved as
new variables in your data file for checking assumptions. Also available are a residual
SSCP matrix, which is a square matrix of sums of squares and cross-products of
residuals, a residual covariance matrix, which is the residual SSCP matrix divided by
the degrees of freedom of the residuals, and the residual correlation matrix, which is
the standardized form of the residual covariance matrix.
WLS Weight allows you to specify a variable used to give observations different
weights for a weighted least-squares (WLS) analysis, perhaps to compensate for
different precision of measurement.
Example. A manufacturer of plastics measures three properties of plastic film: tear
resistance, gloss, and opacity. Two rates of extrusion and two different amounts of
additive are tried, and the three properties are measured under each combination of
extrusion rate and additive amount. The manufacturer finds that the extrusion rate and
the amount of additive individually produce significant results but that the interaction
of the two factors is not significant.
Methods. Type I, Type II, Type III, and Type IV sums of squares can be used to evaluate
different hypotheses. Type III is the default.
Statistics. Post hoc range tests and multiple comparisons: least significant difference,
Bonferroni, Sidak, Scheffé, Ryan-Einot-Gabriel-Welsch multiple F, Ryan-Einot-
Gabriel-Welsch multiple range, Student-Newman-Keuls, Tukey’s honestly significant
difference, Tukey’s-b, Duncan, Hochberg’s GT2, Gabriel, Waller Duncan t test,
Dunnett (one-sided and two-sided), Tamhane’s T2, Dunnett’s T3, Games-Howell, and
Dunnett’s C. Descriptive statistics: observed means, standard deviations, and counts
for all of the dependent variables in all cells; the Levene test for homogeneity of
variance; Box’s M test of the homogeneity of the covariance matrices of the dependent
variables; and Bartlett’s test of sphericity.
Plots. Spread-versus-level, residual, and profile (interaction).
Data. The dependent variables should be quantitative. Factors are categorical and can
have numeric values or string values of up to eight characters. Covariates are
quantitative variables that are related to the dependent variable.
Assumptions. For dependent variables, the data are a random sample of vectors from a
multivariate normal population; in the population, the variance-covariance matrices