paperback. Condizione: Fine.
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Lingua: Inglese
Editore: Guilford Publications, New York, 2023
ISBN 10: 1462552927 ISBN 13: 9781462552924
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Paperback. Condizione: new. Paperback. Today's social and behavioral researchers increasingly need to know: "What do I do with all this data?" This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine learning articles. Techniques are demonstrated using actual data (Big Five Inventory, early childhood learning, and more), with a focus on the interplay of statistical algorithm, data, and theory. The identification of heterogeneity, measurement error, regularization, and decision trees are also emphasized. The book covers basic principles as well as a range of methods for analyzing univariate and multivariate data (factor analysis, structural equation models, and mixed-effects models). Analysis of text and social network data is also addressed. End-of-chapter "Computational Time and Resources" sections include discussions of key R packages; the companion website provides R programming scripts and data for the book's examples. This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine learning articles. Techniques are demonstrated using actual data (Big Five Inventory, early childhood learning, and more), with a focus on the interplay of statistical algorithm, data, and theory. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 89,67
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Aggiungi al carrelloPaperback. Condizione: New. Today's social and behavioral researchers increasingly need to know: "What do I do with all this data?" This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine learning articles. Techniques are demonstrated using actual data (Big Five Inventory, early childhood learning, and more), with a focus on the interplay of statistical algorithm, data, and theory. The identification of heterogeneity, measurement error, regularization, and decision trees are also emphasized. The book covers basic principles as well as a range of methods for analyzing univariate and multivariate data (factor analysis, structural equation models, and mixed-effects models). Analysis of text and social network data is also addressed. End-of-chapter "Computational Time and Resources" sections include discussions of key R packages; the companion website provides R programming scripts and data for the book's examples.
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Prima edizione
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Aggiungi al carrelloPaperback. Condizione: Brand New. 408 pages. 9.75x7.00x0.75 inches. In Stock.
Da: Biblios, Frankfurt am main, HESSE, Germania
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Da: THE SAINT BOOKSTORE, Southport, Regno Unito
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Aggiungi al carrelloPaperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days.
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Aggiungi al carrelloCondizione: NEW.
Lingua: Inglese
Editore: Guilford Publications, New York, 2023
ISBN 10: 1462552927 ISBN 13: 9781462552924
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 79,34
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. Today's social and behavioral researchers increasingly need to know: "What do I do with all this data?" This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine learning articles. Techniques are demonstrated using actual data (Big Five Inventory, early childhood learning, and more), with a focus on the interplay of statistical algorithm, data, and theory. The identification of heterogeneity, measurement error, regularization, and decision trees are also emphasized. The book covers basic principles as well as a range of methods for analyzing univariate and multivariate data (factor analysis, structural equation models, and mixed-effects models). Analysis of text and social network data is also addressed. End-of-chapter "Computational Time and Resources" sections include discussions of key R packages; the companion website provides R programming scripts and data for the book's examples. This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine learning articles. Techniques are demonstrated using actual data (Big Five Inventory, early childhood learning, and more), with a focus on the interplay of statistical algorithm, data, and theory. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Lingua: Inglese
Editore: Guilford Publications, New York, 2023
ISBN 10: 1462552927 ISBN 13: 9781462552924
Da: CitiRetail, Stevenage, Regno Unito
EUR 68,59
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. Today's social and behavioral researchers increasingly need to know: "What do I do with all this data?" This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine learning articles. Techniques are demonstrated using actual data (Big Five Inventory, early childhood learning, and more), with a focus on the interplay of statistical algorithm, data, and theory. The identification of heterogeneity, measurement error, regularization, and decision trees are also emphasized. The book covers basic principles as well as a range of methods for analyzing univariate and multivariate data (factor analysis, structural equation models, and mixed-effects models). Analysis of text and social network data is also addressed. End-of-chapter "Computational Time and Resources" sections include discussions of key R packages; the companion website provides R programming scripts and data for the book's examples. This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine learning articles. Techniques are demonstrated using actual data (Big Five Inventory, early childhood learning, and more), with a focus on the interplay of statistical algorithm, data, and theory. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Da: moluna, Greven, Germania
EUR 74,92
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Aggiungi al carrelloCondizione: New. Ross Jacobucci, PhD, is Assistant Professor in Quantitative Psychology in the Department of Psychology at the University of Notre Dame. His research interests include the development and application of machine learning for clinical research, with a focus.
Condizione: New. 1st edition NO-PA16APR2015-KAP.
Da: Revaluation Books, Exeter, Regno Unito
EUR 119,55
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Aggiungi al carrelloHardcover. Condizione: Brand New. 416 pages. 10.00x7.00x1.25 inches. In Stock.
Da: Biblios, Frankfurt am main, HESSE, Germania
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Da: THE SAINT BOOKSTORE, Southport, Regno Unito
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Aggiungi al carrelloHardback. Condizione: New. New copy - Usually dispatched within 4 working days.
Lingua: Inglese
Editore: Guilford Publications Jul 2023, 2023
ISBN 10: 1462552927 ISBN 13: 9781462552924
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 76,88
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware - This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine learning articles. Techniques are demonstrated using actual data (Big Five Inventory, early childhood learning, and more), with a focus on the interplay of statistical algorithm, data, and theory.
Da: moluna, Greven, Germania
EUR 98,82
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: New. Ross Jacobucci, PhD, is Assistant Professor in Quantitative Psychology in the Department of Psychology at the University of Notre Dame. His research interests include the development and application of machine learning for clinical research, with a focus.