Published by Chapman and Hall/CRC, 2022
ISBN 10: 0367456850 ISBN 13: 9780367456856
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Published by Chapman and Hall/CRC, 2022
ISBN 10: 0367456850 ISBN 13: 9780367456856
Language: English
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Published by Chapman and Hall/CRC, 2022
ISBN 10: 0367456850 ISBN 13: 9780367456856
Language: English
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Published by Taylor & Francis Group, 2022
ISBN 10: 0367456850 ISBN 13: 9780367456856
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Published by Chapman and Hall/CRC, 2022
ISBN 10: 0367456850 ISBN 13: 9780367456856
Language: English
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Published by Chapman and Hall/CRC, 2022
ISBN 10: 0367456850 ISBN 13: 9780367456856
Language: English
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Published by Chapman and Hall/CRC, 2022
ISBN 10: 0367456850 ISBN 13: 9780367456856
Language: English
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Published by Taylor & Francis Group, 2022
ISBN 10: 0367456850 ISBN 13: 9780367456856
Language: English
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Published by Chapman and Hall/CRC, 2022
ISBN 10: 0367456850 ISBN 13: 9780367456856
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Published by Taylor & Francis Group, 2022
ISBN 10: 0367456850 ISBN 13: 9780367456856
Language: English
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Published by Chapman and Hall/CRC, 2022
ISBN 10: 0367456850 ISBN 13: 9780367456856
Language: English
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Add to basketBuch. Condition: 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.
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Add to basketBuch. Condition: 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.