Inhaltsangabe
Explore the geometry of spaces beyond three dimensions with clear, math-grounded explanations that connect to statistics.
This volume presents a practical introduction to the geometry of n dimensions, focused on ideas with direct statistical applications. It blends coordinate geometry with invariant reasoning and shows how higher-dimensional concepts illuminate problems in data analysis, multivariate distribution, and related fields. The book is organized into two parts: a thorough treatment of n-dimensional geometry and a second part that applies those ideas to statistical methods, including correlation, regression, and factor-related techniques.
- Understand the foundations of n-dimensional geometry, including distance, angles, projections, and dualities.
- Learn how to reduce complex quadrics and interpret canonical forms in a statistical context.
- See how geometric thinking underpins regression, correlation, canonical correlation, and component analysis.
- Explore how concepts like hyperspheres, content, and projections relate to real data problems.
Ideal for readers of advanced statistics and geometry who want a cohesive, application-minded introduction to higher-dimensional thinking.
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