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Introduction General Generalized Linear Models by Madsen (27 results)
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Introduction to General and Generalized Linear Models (Paperback)
Published by Taylor & Francis Ltd, London, 2024
ISBN 10: 1032922362 ISBN 13: 9781032922362
Language: English
Paperback. Condition: new. Paperback. Bridging the gap between theory and practice for modern statistical model building, Introduction to General and Generalized Linear Models presents likelihood-based techniques for statistical modelling using various types of data. Implementations using R are provided throughout the text, although other software packages are also discussed. Numerous examples show how the problems are solved with R.After describing the necessary likelihood theory, the book covers both general and generalized linear models using the same likelihood-based methods. It presents the corresponding/parallel results for the general linear models first, since they are easier to understand and often more well known. The authors then explore random effects and mixed effects in a Gaussian context. They also introduce non-Gaussian hierarchical models that are members of the exponential family of distributions. Each chapter contains examples and guidelines for solving the problems via R. Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to general and generalized linear models using the popular and powerful likelihood techniques. Ancillary materials are available at /~hm/GLM Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to general and generalized linear models using the popular and powerful likelihood techniques. The aut Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Introduction to General and Generalized Linear Models
Published by Taylor & Francis Group, 2010
ISBN 10: 1420091557 ISBN 13: 9781420091557
Language: English
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Introduction to General and Generalized Linear Models
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Introduction to General and Generalized Linear Models (Chapman & Hall/CRC Texts in Statistical Science)
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Introduction to General and Generalized Linear Models
Published by Taylor & Francis Group, 2010
ISBN 10: 1420091557 ISBN 13: 9781420091557
Language: English
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Introduction to General and Generalized Linear Models
Published by Taylor & Francis Ltd, 2024
ISBN 10: 1032922362 ISBN 13: 9781032922362
Language: English
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Introduction To General And Generalized Linear Models
Published by Crc Press, 2020
Language: English
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Introduction to General and Generalized Linear Models
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Introduction to General and Generalized Linear Models (Chapman & Hall/CRC Texts in Statistical Science)
Published by CRC Press 2010-11-19, 2010
ISBN 10: 1420091557 ISBN 13: 9781420091557
Language: English
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Introduction to General and Generalized Linear Models
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Introduction to General and Generalized Linear Models
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Add to basketHardcover. Condition: Brand New. 1st edition. 316 pages. 10.00x6.75x1.00 inches. In Stock.
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Introduction to General and Generalized Linear Models (Paperback)
Published by Taylor & Francis Ltd, London, 2024
ISBN 10: 1032922362 ISBN 13: 9781032922362
Language: English
US$ 81.85
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Add to basketPaperback. Condition: new. Paperback. Bridging the gap between theory and practice for modern statistical model building, Introduction to General and Generalized Linear Models presents likelihood-based techniques for statistical modelling using various types of data. Implementations using R are provided throughout the text, although other software packages are also discussed. Numerous examples show how the problems are solved with R.After describing the necessary likelihood theory, the book covers both general and generalized linear models using the same likelihood-based methods. It presents the corresponding/parallel results for the general linear models first, since they are easier to understand and often more well known. The authors then explore random effects and mixed effects in a Gaussian context. They also introduce non-Gaussian hierarchical models that are members of the exponential family of distributions. Each chapter contains examples and guidelines for solving the problems via R. Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to general and generalized linear models using the popular and powerful likelihood techniques. Ancillary materials are available at /~hm/GLM Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to general and generalized linear models using the popular and powerful likelihood techniques. The aut Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Introduction to General and Generalized Linear Models (Paperback)
Published by Taylor & Francis Ltd, London, 2024
ISBN 10: 1032922362 ISBN 13: 9781032922362
Language: English
US$ 104.86
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Add to basketPaperback. Condition: new. Paperback. Bridging the gap between theory and practice for modern statistical model building, Introduction to General and Generalized Linear Models presents likelihood-based techniques for statistical modelling using various types of data. Implementations using R are provided throughout the text, although other software packages are also discussed. Numerous examples show how the problems are solved with R.After describing the necessary likelihood theory, the book covers both general and generalized linear models using the same likelihood-based methods. It presents the corresponding/parallel results for the general linear models first, since they are easier to understand and often more well known. The authors then explore random effects and mixed effects in a Gaussian context. They also introduce non-Gaussian hierarchical models that are members of the exponential family of distributions. Each chapter contains examples and guidelines for solving the problems via R. Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to general and generalized linear models using the popular and powerful likelihood techniques. Ancillary materials are available at /~hm/GLM Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to general and generalized linear models using the popular and powerful likelihood techniques. The aut Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Add to basketPaperback. Condition: Brand New. 316 pages. 9.18x6.12x0.59 inches. In Stock. This item is printed on demand.
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Introduction to General and Generalized Linear Models
Published by Taylor & Francis Group, 2010
ISBN 10: 1420091557 ISBN 13: 9781420091557
Language: English
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Introduction to General and Generalized Linear Models
Published by Taylor & Francis, CRC Press, 2010
ISBN 10: 1420091557 ISBN 13: 9781420091557
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Add to basketBuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Bridging the gap between theory and practice for modern statistical model building, Introduction to General and Generalized Linear Models presents likelihood-based techniques for statistical modelling using various types of data. Implementations using R are provided throughout the text, although other software packages are also discussed. Numerous examples show how the problems are solved with R.After describing the necessary likelihood theory, the book covers both general and generalized linear models using the same likelihood-based methods. It presents the corresponding/parallel results for the general linear models first, since they are easier to understand and often more well known. The authors then explore random effects and mixed effects in a Gaussian context. They also introduce non-Gaussian hierarchical models that are members of the exponential family of distributions. Each chapter contains examples and guidelines for solving the problems via R. Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to general and generalized linear models using the popular and powerful likelihood techniques. Ancillary materials are available at imm.dtu.dk/~hm/GLM 316 pp. Englisch.
