Geometric data analysis: from correspond
β Brigitte Le Roux, Henry Rouanet
π Library
π
2004
π Springer
π English
β¦ LIBER β¦
π
Geometric data analysis: from correspondence analysis to structured data analysis
β Scribed by Le Roux B., Rouanet H.
- Publisher
- Kluwer
- Year
- 2004
- Tongue
- English
- Leaves
- 488
- Category
- Library
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β¦ Synopsis
Geometric Data Analysis (GDA) is the name suggested by P. Suppes (Stanford University) to designate the approach to Multivariate Statistics initiated by BenzΠΒ©cri as Correspondence Analysis, an approach that has become more and more used and appreciated over the years. This book presents the full formalization of GDA in terms of linear algebra - the most original and far-reaching consequential feature of the approach - and shows also how to integrate the standard statistical tools such as Analysis of Variance, including Bayesian methods. Chapter 9, Research Case Studies, is nearly a book in itself; it presents the methodology in action on three extensive applications, one for medicine, one from political science, and one from education (data borrowed from the Stanford computer-based Educational Program for Gifted Youth ). Thus the readership of the book concerns both mathematicians interested in the applications of mathematics, and researchers willing to master an exceptionally powerful approach of statistical data analysis
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Geometric Data Analysis: From Correspond
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<p><STRONG>Geometric Data Analysis</STRONG> (GDA) is the name suggested by P. Suppes (Stanford University) to designate the approach to Multivariate Statistics initiated by BenzΓ©cri as Correspondence Analysis, an approach that has become more and more used and appreciated over the years. This book p
Geometric Data Analysis: From Correspond
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π Library
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2004
π Springer
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<span>Geometric Data Analysis</span><span> (GDA) is the name suggested by P. Suppes (Stanford University) to designate the approach to Multivariate Statistics initiated by BenzΓ©cri as Correspondence Analysis, an approach that has become more and more used and appreciated over the years. This book pr
Geometric Data Analysis: From Correspond
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π Library
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