Michael Greenacre's Correspondence Analysis in Practice, Third Edition PDF

By Michael Greenacre

ISBN-10: 1315352958

ISBN-13: 9781315352954

ISBN-10: 1498731775

ISBN-13: 9781498731775

ISBN-10: 1498731783

ISBN-13: 9781498731782

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 information visualization in a large choice of occasions. CA and its editions, subset CA, a number of CA and joint CA, translate two-way and multi-way tables into extra readable graphical types ― perfect for functions within the social, environmental and wellbeing and fitness 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 information Visualization. He has authored and co-edited 9 books and eighty magazine articles and e-book chapters, totally on correspondence research, the most recent being Visualization and Verbalization of Data in 2015. He has given brief classes in fifteen international locations to environmental scientists, sociologists, information scientists and advertising pros, and has really good in information in ecology and social science.

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Additional resources for Correspondence Analysis in Practice, Third Edition

Example text

5. The inertia can be rewritten in a form which can be interpreted as the weighted average of squared χ2 -distances between the row profiles and their average profile (similarly, between the column profiles and their average). 5 Plotting Chi-Square Distances In Chapter 3 we interpreted the positions of two-dimensional profile points in a triangular coordinate system where distances were Euclidean distances. In Chapter 4 the chi-square distance (χ2 -distance) between profile points was defined, as well as its connection with the chi-square statistic and the inertia of a data matrix.

4) is not the Euclidean distance — it involves an extra factor in the denominator of each squared term. Because this factor rescales or reweights each squared difference term, this variant of the Euclidean distance function is referred to in general as a weighted Euclidean distance. In this particular case where the scaling factors in the denominators are the expected profile elements, the distance is called the chi-square distance, or χ2 -distance for short. 5) because each term under the square root sign has been increased in value.

This question is answered by computing a measure of discrepancy between all the observed and expected frequencies, as follows. Each difference between an observed and expected frequency is computed, then this difference is squared and finally divided by the expected frequency. This calculation is repeated for all pairs of observed and expected frequencies and the results are accumulated into a single figure — the chi-square statistic, Calculating the χ2 statistic denoted by χ2 : χ2 = 27 (observed − expected)2 expected Because there are 15 cells in this 5-by-3 (or 5 × 3) table, there will be 15 terms in this computation.

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Correspondence Analysis in Practice, Third Edition by Michael Greenacre

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