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Book
Year: 1989 Publisher: Newbury Park: Sage,
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Book
ISBN: 9781412985475 Year: 1989 Publisher: Newbury Park, [Calif.] ; London : SAGE,
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Principal components analysis offers researchers a 'feel' for analysing particular sets of multidimensional data. It is particularly useful in coping with multicolinearity in regression analysis, a persistent problem in behavioral and social science data sets.
Book
Year: 1985 Publisher: Leuven: KUL. Geografisch instituut,
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ISBN: 9780471471349 0471471348 Year: 2003 Publisher: Hoboken, N.J.: Wiley,
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Book
ISBN: 0444430148 Year: 1988 Publisher: Amsterdam : Elsevier,
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Meteorology --- Principal components analysis --- Oceanography --- Methodology --- Methodology.
Book
Year: 1985 Publisher: Leuven : KUL. Geografisch instituut,
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Human geography --- Principal components analysis. --- Statistical methods.
Book
Year: 2022 Publisher: London, England : IntechOpen,
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This book describes and discusses the use of principal component analysis (PCA) for different types of problems in a variety of disciplines, including engineering, technology, economics, and more. It presents real-world case studies showing how PCA can be applied with other algorithms and methods to solve both large and small and static and dynamic problems. It also examines improvements made to PCA over the years.
Book
ISBN: 0803930142 Year: 1989 Volume: 07-069 Publisher: Newbury Park : Sage Publications,
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Book
Year: 2022 Publisher: London, England : IntechOpen,
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This book describes and discusses the use of principal component analysis (PCA) for different types of problems in a variety of disciplines, including engineering, technology, economics, and more. It presents real-world case studies showing how PCA can be applied with other algorithms and methods to solve both large and small and static and dynamic problems. It also examines improvements made to PCA over the years.
Book
Year: 2022 Publisher: London, England : IntechOpen,
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This book describes and discusses the use of principal component analysis (PCA) for different types of problems in a variety of disciplines, including engineering, technology, economics, and more. It presents real-world case studies showing how PCA can be applied with other algorithms and methods to solve both large and small and static and dynamic problems. It also examines improvements made to PCA over the years.
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