By Michael Greenacre

Drawing at the author’s forty five years of expertise in multivariate research, **Correspondence research in perform, 3rd version, **shows how the flexible approach to correspondence research (CA) can be utilized for facts visualization in a wide selection of occasions. CA and its versions, subset CA, a number of CA and joint CA, translate two-way and multi-way tables into extra readable graphical varieties ― excellent for purposes within the social, environmental and health and wellbeing sciences, in addition to advertising, economics, linguistics, archaeology, and more.

**Michael Greenacre **is Professor of facts on the Universitat Pompeu Fabra, Barcelona, Spain, the place he teaches a direction, among others, on facts Visualization. He has authored and co-edited 9 books and eighty magazine articles and booklet chapters, totally on correspondence research, the most recent being *Visualization and Verbalization of Data* in 2015. He has given brief classes in fifteen nations to environmental scientists, sociologists, information scientists and advertising and marketing pros, and has really expert in records in ecology and social science.

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**Extra resources for Correspondence Analysis in Practice, Third Edition**

**Example text**

J) are calculated by summing the squared differences between coordinates, of the form (xj − yj )2 , over all dimensions j and then taking the square root of the resultant sum. This is the usual “straight-line” Euclidean distance in physical space with which we are familiar. e. each term is of the form (xj − yj )2 /cj , where cj is the corresponding element of the average profile. 1: The profile space showing the profiles of the education groups on the equilateral triangle in three-dimensional space; the distances here are Euclidean distances.

4. 7) that the weighted sum of these squared distances is identical to the inertia of the table. If we associate the row totals with the profiles rather than the masses (where each row total is n times the respective row mass, n being the grand total of the whole table), then the weighted sum of these squared distances is equal to the χ2 statistic. Equivalent results hold for the column profiles relative to their average point. e. masses) or total frequencies respectively.

Each of these squared distances is the sum of three values, one term for each column category, and can be expressed as percentages of the total to assess the contributions of each category of readership. e. 5). 5). This exercise illustrates the phenomenon that the lowest frequency category C1 generally contributes less to the Euclidean distance compared to C3 , for example, whereas in the χ2 -distance its contribution is boosted owing to the division by the average frequency. 1). 1) j=1 where wj are nonnegative weights and xj , j = 1, .