Perfected over three editions and more than forty years, this field- and classroom-tested reference: * Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures. * Treats all the basic and
Its overarching goal is to provide readers with the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. Chapter topics include aspects of multivariate analysis, matrix algebra and ran
Although there are several good books on principal component methods and related topics, we felt that many of them are either too theoretical or too advanced. Our goal was to write a practical guide to multivariate analysis, visualization and inter-
