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Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
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Vendeur : Majestic Books, Hounslow, Royaume-Uni
EUR 122,99
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Ajouter au panierEtat : New. pp. 270.
Vendeur : California Books, Miami, FL, Etats-Unis
EUR 131,65
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Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
EUR 116,70
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Vendeur : Books Puddle, New York, NY, Etats-Unis
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Ajouter au panierEtat : New. pp. 270.
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
EUR 126,72
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Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Ajouter au panierEtat : New. pp. 270.
Vendeur : Russell Books, Victoria, BC, Canada
Edition originale
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Ajouter au panierhardcover. Etat : New. 1st Edition. Special order direct from the distributor.
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Ajouter au panierHardcover. Etat : Brand New. 272 pages. 9.00x6.00x0.75 inches. In Stock.
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Ajouter au panierHardcover. Etat : Brand New. 272 pages. 9.00x6.00x0.75 inches. In Stock. This item is printed on demand.
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 111,70
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Ajouter au panierBuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Introduction to Design and Analysis of Scientific Studies exposes undergraduate and graduate students to the foundations of classical experimental design and observational studies through a modern framework - The Rubin Causal Model. A causal inference framework is important in design, data collection and analysis since it provides a framework for investigators to readily evaluate study limitations and draw appropriate conclusions. R is used to implement designs and analyse the data collected.Features:Classical experimental design with an emphasis on computation using tidyverse packages in R.Applications of experimental design to clinical trials, A/B testing, and other modern examples.Discussion of the link between classical experimental design and causal inference.The role of randomization in experimental design and sampling in the big data era.Exercises with solutions.Instructor slides in RMarkdown, a new R package will be developed to be used with book, and a bookdown version of the book will be freely available. The proposed book will emphasize ethics, communication and decision making as part of design, data analysis, and statistical thinking. 292 pp. Englisch.
Vendeur : PBShop.store US, Wood Dale, IL, Etats-Unis
EUR 141,01
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Ajouter au panierHRD. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
EUR 135,22
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Ajouter au panierHRD. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 123,56
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Ajouter au panierBuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Introduction to Design and Analysis of Scientific Studies exposes undergraduate and graduate students to the foundations of classical experimental design and observational studies through a modern framework - The Rubin Causal Model. A causal inference framework is important in design, data collection and analysis since it provides a framework for investigators to readily evaluate study limitations and draw appropriate conclusions. R is used to implement designs and analyse the data collected.Features:Classical experimental design with an emphasis on computation using tidyverse packages in R.Applications of experimental design to clinical trials, A/B testing, and other modern examples.Discussion of the link between classical experimental design and causal inference.The role of randomization in experimental design and sampling in the big data era.Exercises with solutions.Instructor slides in RMarkdown, a new R package will be developed to be used with book, and a bookdown version of the book will be freely available. The proposed book will emphasize ethics, communication and decision making as part of design, data analysis, and statistical thinking.