内容

目次

Introduction. Part 1 Looking at multivariate data: Motivation andfundamental concepts; one-way graphical representation of datamatrices; graphical methods for association or proximity matrices;two-way graphical representation of data matrices; analyticalcomparison of two or more graphical representations. Part 2 Samples,populations and models: Data inspection or data analysis; distributiontheory. Part 3 Analyzing ungrouped data: Estimation and hypothesistesting; reduction of dimensionality - inferential aspects ofdescriptive methods; discrete data. Part 4 Analyzing grouped data:Incorporating group structure - descriptive statistics; inferentialaspects - the two-group case; inferential aspects - more than twogroups. Part 5 Analyzing association among variables: Measuring andinterpreting association; exploiting observed associations -manifest-variable models; explaining observed associations -latent-variable models. Conclusion - some general multivariateproblems. Appendix - some basic matrix theory. References. Index.