A guide for biologists describing statistical methods for testing causal hypotheses using R and their application to biological examples.
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Bill Shipley is Associate Professor within the Department of Biology at Université de Sherbrooke, Canada. He is the author of From Plant Traits to Vegetation Structure: Chance and Selection in the Assembly of Ecological Communities (2012) and continues to make significant contributions to statistical methodology in ecology.
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Paperback. Condition: new. Paperback. Aimed at practising biologists, especially graduate students and researchers in ecology, this revised and expanded 3rd edition continues to explore cause-effect relationships through a series of robust statistical methods. Every chapter has been updated, and two brand-new chapters cover statistical power, Akaike information criterion statistics and equivalent models, and piecewise structural equation modelling with implicit latent variables. A new R package (pwSEM) is included to assist with the latter. The book offers advanced coverage of essential topics, including d-separation tests and path analysis, and equips biologists with the tools needed to carry out analyses in the open-source R statistical environment. Writing in a conversational style that minimises technical jargon, Shipley offers an accessible text that assumes only a very basic knowledge of introductory statistics, incorporating real-world examples that allow readers to make connections between biological phenomena and the underlying statistical concepts. Written for graduate students and researchers in biology without advanced statistical training, this book explains the process of testing cause-and effect hypotheses using statistical tools. Combining both theory and practice, learners are provided with step-by-step explanations for analysing data, using the dedicated R packages. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781009560382
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Paperback. Condition: New. Aimed at practising biologists, especially graduate students and researchers in ecology, this revised and expanded 3rd edition continues to explore cause-effect relationships through a series of robust statistical methods. Every chapter has been updated, and two brand-new chapters cover statistical power, Akaike information criterion statistics and equivalent models, and piecewise structural equation modelling with implicit latent variables. A new R package (pwSEM) is included to assist with the latter. The book offers advanced coverage of essential topics, including d-separation tests and path analysis, and equips biologists with the tools needed to carry out analyses in the open-source R statistical environment. Writing in a conversational style that minimises technical jargon, Shipley offers an accessible text that assumes only a very basic knowledge of introductory statistics, incorporating real-world examples that allow readers to make connections between biological phenomena and the underlying statistical concepts. Seller Inventory # LU-9781009560382
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Paperback. Condition: new. Paperback. Aimed at practising biologists, especially graduate students and researchers in ecology, this revised and expanded 3rd edition continues to explore cause-effect relationships through a series of robust statistical methods. Every chapter has been updated, and two brand-new chapters cover statistical power, Akaike information criterion statistics and equivalent models, and piecewise structural equation modelling with implicit latent variables. A new R package (pwSEM) is included to assist with the latter. The book offers advanced coverage of essential topics, including d-separation tests and path analysis, and equips biologists with the tools needed to carry out analyses in the open-source R statistical environment. Writing in a conversational style that minimises technical jargon, Shipley offers an accessible text that assumes only a very basic knowledge of introductory statistics, incorporating real-world examples that allow readers to make connections between biological phenomena and the underlying statistical concepts. Written for graduate students and researchers in biology without advanced statistical training, this book explains the process of testing cause-and effect hypotheses using statistical tools. Combining both theory and practice, learners are provided with step-by-step explanations for analysing data, using the dedicated R packages. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781009560382
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