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Published by Cambridge University Press, 2018
ISBN 10: 1107185823 ISBN 13: 9781107185821
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Published by Cambridge University Press, 2018
ISBN 10: 1107185823 ISBN 13: 9781107185821
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Language: English
Published by Cambridge University Press, 2018
ISBN 10: 1107185823 ISBN 13: 9781107185821
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Language: English
Published by Cambridge University Press, 2018
ISBN 10: 1107185823 ISBN 13: 9781107185821
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Published by Cambridge University Press, 2018
ISBN 10: 1107185823 ISBN 13: 9781107185821
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Published by Cambridge University Press, 2018
ISBN 10: 1107185823 ISBN 13: 9781107185821
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Language: English
Published by Cambridge University Press, 2018
ISBN 10: 1107185823 ISBN 13: 9781107185821
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Language: English
Published by Cambridge University Press, 2019
ISBN 10: 1107185823 ISBN 13: 9781107185821
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Published by Cambridge University Press CUP, 2018
ISBN 10: 1107185823 ISBN 13: 9781107185821
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Published by Cambridge University Press, 2018
ISBN 10: 1107185823 ISBN 13: 9781107185821
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Seller: Revaluation Books, Exeter, United Kingdom
Hardcover. Condition: Brand New. 298 pages. 9.25x6.25x1.00 inches. In Stock.
Language: English
Published by Cambridge University Press, 2018
ISBN 10: 1107185823 ISBN 13: 9781107185821
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Practical, example-driven introduction to maximum likelihood for the social sciences. Emphasizes computation in R, model selection and interpretation.
Language: English
Published by Cambridge University Press, Cambridge, 2018
ISBN 10: 1107185823 ISBN 13: 9781107185821
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Hardcover. Condition: new. Hardcover. This volume provides a practical introduction to the method of maximum likelihood as used in social science research. Ward and Ahlquist focus on applied computation in R and use real social science data from actual, published research. Unique among books at this level, it develops simulation-based tools for model evaluation and selection alongside statistical inference. The book covers standard models for categorical data as well as counts, duration data, and strategies for dealing with data missingness. By working through examples, math, and code, the authors build an understanding about the contexts in which maximum likelihood methods are useful and develop skills in translating mathematical statements into executable computer code. Readers will not only be taught to use likelihood-based tools and generate meaningful interpretations, but they will also acquire a solid foundation for continued study of more advanced statistical techniques. A new advanced text in applied statistics and methodology in the social sciences, aimed at Ph.D. students in political science, sociology, and related disciplines. The authors take an applied perspective here, emphasizing core statistical concepts, computation in R, and the tools for evaluating and interpreting statistical models. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Hardcover. Condition: Brand New. 298 pages. 9.25x6.25x1.00 inches. In Stock. This item is printed on demand.
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Published by Cambridge University Press, 2018
ISBN 10: 1107185823 ISBN 13: 9781107185821
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Add to basketHardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Language: English
Published by Cambridge University Press, 2018
ISBN 10: 1107185823 ISBN 13: 9781107185821
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Language: English
Published by Cambridge University Press, Cambridge, 2018
ISBN 10: 1107185823 ISBN 13: 9781107185821
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Hardcover. Condition: new. Hardcover. This volume provides a practical introduction to the method of maximum likelihood as used in social science research. Ward and Ahlquist focus on applied computation in R and use real social science data from actual, published research. Unique among books at this level, it develops simulation-based tools for model evaluation and selection alongside statistical inference. The book covers standard models for categorical data as well as counts, duration data, and strategies for dealing with data missingness. By working through examples, math, and code, the authors build an understanding about the contexts in which maximum likelihood methods are useful and develop skills in translating mathematical statements into executable computer code. Readers will not only be taught to use likelihood-based tools and generate meaningful interpretations, but they will also acquire a solid foundation for continued study of more advanced statistical techniques. A new advanced text in applied statistics and methodology in the social sciences, aimed at Ph.D. students in political science, sociology, and related disciplines. The authors take an applied perspective here, emphasizing core statistical concepts, computation in R, and the tools for evaluating and interpreting statistical models. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Language: English
Published by Cambridge University Press, 2018
ISBN 10: 1107185823 ISBN 13: 9781107185821
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. A new advanced text in applied statistics and methodology in the social sciences, aimed at Ph.D. students in political science, sociology, and related disciplines. The authors take an applied perspective here, emphasizing core statistical concepts, computat.
Language: English
Published by Cambridge University Press, Cambridge, 2018
ISBN 10: 1107185823 ISBN 13: 9781107185821
Seller: AussieBookSeller, Truganina, VIC, Australia
Hardcover. Condition: new. Hardcover. This volume provides a practical introduction to the method of maximum likelihood as used in social science research. Ward and Ahlquist focus on applied computation in R and use real social science data from actual, published research. Unique among books at this level, it develops simulation-based tools for model evaluation and selection alongside statistical inference. The book covers standard models for categorical data as well as counts, duration data, and strategies for dealing with data missingness. By working through examples, math, and code, the authors build an understanding about the contexts in which maximum likelihood methods are useful and develop skills in translating mathematical statements into executable computer code. Readers will not only be taught to use likelihood-based tools and generate meaningful interpretations, but they will also acquire a solid foundation for continued study of more advanced statistical techniques. A new advanced text in applied statistics and methodology in the social sciences, aimed at Ph.D. students in political science, sociology, and related disciplines. The authors take an applied perspective here, emphasizing core statistical concepts, computation in R, and the tools for evaluating and interpreting statistical models. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.