Titlebook: Exploring Multivariate Data with the Forward Search; Anthony C. Atkinson,Marco Riani,Andrea Cerioli Book 2004 Springer Science+Business Me

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Examples of Multivariate Data,of all our data sets, an . matrix representing . observations on each of . units, here people. In most examples we first look at a scatterplot matrix of the data and then fit a multivariate normal distribution. This model can be fitted to any such rectangular array of numbers, so we need to explore

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Data from One Multivariate Distribution, the Swiss heads data to exemplify the properties of bivariate boxplots for data analysis. As a preparation for material on transformations of data in Chapter 4 we compare analyses of the data on national track records for women when the response is the time for the race and also its reciprocal, spe

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Discriminant Analysis,f the analysis is to develop a rule for the allocation of a new observation of unknown origin to the most likely group. For example, in the case of the Swiss bank notes there are two groups, genuine notes and forgeries. The purpose of the analysis would be to develop a rule for determining whether o

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Cluster Analysis,ups. The situation is seemingly different from that of discriminant analysis considered in Chapter 6 where both the number of groups and group membership are known. However, there is much in common between our procedure for clustering and the methods we used in the earlier chapters.

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Spatial Linear Models,in space. By far the most common applications have been on the earth’s surface, where each location can be described by a two-dimensional vector of geographical coordinates. One example is the analysis of yield from spatially contiguous plots in an experimental design, when the plots are subject to

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