Optimal Discriminant Analysis: Linear Discriminant Analysis, Analysis of Variance, Regression Analysis, Dependent Variable - Softcover
Synopsis
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Optimal discriminant analysis (ODA) and the related classification tree analysis (CTA) are statistical methods that maximize predictive accuracy. For any specific sample and exploratory or confirmatory hypothesis, optimal discriminant analysis (ODA) identifies the statistical model that yields maximum predictive accuracy, assesses the exact Type I error rate, and evaluates potential cross-generalizability. Optimal discriminant analysis may be applied to > 0 dimensions, with the one-dimensional case being referred to as UniODA and the multidimensional case being referred to as MultiODA. Classification tree analysis is a generalization of Optimal Discriminant Analysis to non-orthogonal trees. Optimal Discriminant Analysis and Classification Tree Analysis may be used to find the combination of variables and cut points that best separate classes of objects or events. These variables and cut points may then be used to reduce dimensions and to then build a statistical model that optimally describes the data.
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Reseña del editor
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Optimal discriminant analysis (ODA) and the related classification tree analysis (CTA) are statistical methods that maximize predictive accuracy. For any specific sample and exploratory or confirmatory hypothesis, optimal discriminant analysis (ODA) identifies the statistical model that yields maximum predictive accuracy, assesses the exact Type I error rate, and evaluates potential cross-generalizability. Optimal discriminant analysis may be applied to > 0 dimensions, with the one-dimensional case being referred to as UniODA and the multidimensional case being referred to as MultiODA. Classification tree analysis is a generalization of Optimal Discriminant Analysis to non-orthogonal trees. Optimal Discriminant Analysis and Classification Tree Analysis may be used to find the combination of variables and cut points that best separate classes of objects or events. These variables and cut points may then be used to reduce dimensions and to then build a statistical model that optimally describes the data.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -High Quality Content by WIKIPEDIA articles! Optimal discriminant analysis (ODA) and the related classification tree analysis (CTA) are statistical methods that maximize predictive accuracy. For any specific sample and exploratory or confirmatory hypothesis, optimal discriminant analysis (ODA) identifies the statistical model that yields maximum predictive accuracy, assesses the exact Type I error rate, and evaluates potential cross-generalizability. Optimal discriminant analysis may be applied to 0 dimensions, with the one-dimensional case being referred to as UniODA and the multidimensional case being referred to as MultiODA. Classification tree analysis is a generalization of Optimal Discriminant Analysis to non-orthogonal trees. Optimal Discriminant Analysis and Classification Tree Analysis may be used to find the combination of variables and cut points that best separate classes of objects or events. These variables and cut points may then be used to reduce dimensions and to then build a statistical model that optimally describes the data. 84 pp. Englisch. Seller Inventory # 9786130499662
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Optimal Discriminant Analysis : Linear Discriminant Analysis, Analysis of Variance, Regression Analysis, Dependent Variable
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - High Quality Content by WIKIPEDIA articles! Optimal discriminant analysis (ODA) and the related classification tree analysis (CTA) are statistical methods that maximize predictive accuracy. For any specific sample and exploratory or confirmatory hypothesis, optimal discriminant analysis (ODA) identifies the statistical model that yields maximum predictive accuracy, assesses the exact Type I error rate, and evaluates potential cross-generalizability. Optimal discriminant analysis may be applied to 0 dimensions, with the one-dimensional case being referred to as UniODA and the multidimensional case being referred to as MultiODA. Classification tree analysis is a generalization of Optimal Discriminant Analysis to non-orthogonal trees. Optimal Discriminant Analysis and Classification Tree Analysis may be used to find the combination of variables and cut points that best separate classes of objects or events. These variables and cut points may then be used to reduce dimensions and to then build a statistical model that optimally describes the data. Seller Inventory # 9786130499662
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