Verlag: Inst of Mathematical Statistic, 1988
ISBN 10: 0940600137 ISBN 13: 9780940600133
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 0.7.
Verlag: Institute of Mathematical Statistics, 1988
ISBN 10: 0940600137 ISBN 13: 9780940600133
Sprache: Englisch
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In den WarenkorbPaperback or Softback. Zustand: New. Non-Experimental Data, Hypothesis Testing, and the Likelihood Principle: A Social Science Perspective 0.27. Book.
Verlag: Institute of Mathematical Statistics, 1984
ISBN 10: 0940600064 ISBN 13: 9780940600065
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: New. Non-Experimental Data, Hypothesis Testing, and the Likelihood Principle: A Social Science Perspective argues that frequentist hypothesis testing - the dominant statistical evaluation paradigm in empirical research - is fundamentally unsuited for analysis of the non-experimental data prevalent in economics and other social sciences. Frequentist tests comprise incompatible repeated sampling frameworks that do not obey the Likelihood Principle (LP). For probabilistic inference, methods that are guided by the LP, that do not rely on repeated sampling, and that focus on model comparison instead of testing (e.g., subjectivist Bayesian methods) are better suited for passively observed social science data and are better able to accommodate the huge model uncertainty and highly approximative nature of structural models in the social sciences. In addition to formal probabilistic inference, informal model evaluation along relevant substantive and practical dimensions should play a leading role. The authors sketch the ideas of an alternative paradigm containing these elements.
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In den WarenkorbPaperback. Zustand: New. Non-Experimental Data, Hypothesis Testing, and the Likelihood Principle: A Social Science Perspective argues that frequentist hypothesis testing - the dominant statistical evaluation paradigm in empirical research - is fundamentally unsuited for analysis of the non-experimental data prevalent in economics and other social sciences. Frequentist tests comprise incompatible repeated sampling frameworks that do not obey the Likelihood Principle (LP). For probabilistic inference, methods that are guided by the LP, that do not rely on repeated sampling, and that focus on model comparison instead of testing (e.g., subjectivist Bayesian methods) are better suited for passively observed social science data and are better able to accommodate the huge model uncertainty and highly approximative nature of structural models in the social sciences. In addition to formal probabilistic inference, informal model evaluation along relevant substantive and practical dimensions should play a leading role. The authors sketch the ideas of an alternative paradigm containing these elements.
Verlag: Inst of Mathematical Statistic, 1988
ISBN 10: 0940600137 ISBN 13: 9780940600133
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: New. Non-Experimental Data, Hypothesis Testing, and the Likelihood Principle: A Social Science Perspective argues that frequentist hypothesis testing - the dominant statistical evaluation paradigm in empirical research - is fundamentally unsuited for analysis of the non-experimental data prevalent in economics and other social sciences. Frequentist tests comprise incompatible repeated sampling frameworks that do not obey the Likelihood Principle (LP). For probabilistic inference, methods that are guided by the LP, that do not rely on repeated sampling, and that focus on model comparison instead of testing (e.g., subjectivist Bayesian methods) are better suited for passively observed social science data and are better able to accommodate the huge model uncertainty and highly approximative nature of structural models in the social sciences. In addition to formal probabilistic inference, informal model evaluation along relevant substantive and practical dimensions should play a leading role. The authors sketch the ideas of an alternative paradigm containing these elements.
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In den WarenkorbPaperback. Zustand: New. Non-Experimental Data, Hypothesis Testing, and the Likelihood Principle: A Social Science Perspective argues that frequentist hypothesis testing - the dominant statistical evaluation paradigm in empirical research - is fundamentally unsuited for analysis of the non-experimental data prevalent in economics and other social sciences. Frequentist tests comprise incompatible repeated sampling frameworks that do not obey the Likelihood Principle (LP). For probabilistic inference, methods that are guided by the LP, that do not rely on repeated sampling, and that focus on model comparison instead of testing (e.g., subjectivist Bayesian methods) are better suited for passively observed social science data and are better able to accommodate the huge model uncertainty and highly approximative nature of structural models in the social sciences. In addition to formal probabilistic inference, informal model evaluation along relevant substantive and practical dimensions should play a leading role. The authors sketch the ideas of an alternative paradigm containing these elements.
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ISBN 10: 1638283249 ISBN 13: 9781638283249
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In den WarenkorbPaperback. Zustand: new. Paperback. Non-Experimental Data, Hypothesis Testing, and the Likelihood Principle: A Social Science Perspective argues that frequentist hypothesis testing - the dominant statistical evaluation paradigm in empirical research - is fundamentally unsuited for analysis of the non-experimental data prevalent in economics and other social sciences. Frequentist tests comprise incompatible repeated sampling frameworks that do not obey the Likelihood Principle (LP). For probabilistic inference, methods that are guided by the LP, that do not rely on repeated sampling, and that focus on model comparison instead of testing (e.g., subjectivist Bayesian methods) are better suited for passively observed social science data and are better able to accommodate the huge model uncertainty and highly approximative nature of structural models in the social sciences. In addition to formal probabilistic inference, informal model evaluation along relevant substantive and practical dimensions should play a leading role. The authors sketch the ideas of an alternative paradigm containing these elements. Argues that frequentist hypothesis testing - the dominant statistical evaluation paradigm in empirical research - is fundamentally unsuited for analysis of the non-experimental data prevalent in economics and other social sciences. The authors sketch the ideas of an alternative paradigm. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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In den WarenkorbBuch. Zustand: Neu. Neuware - Evidence-Based Statistics: An Introduction to the Evidential Approach - from Likelihood Principle to Statistical Practice provides readers with a comprehensive and thorough guide to the evidential approach in statistics. The approach uses likelihood ratios, rather than the probabilities used by other statistical inference approaches. The evidential approach is conceptually easier to grasp, and the calculations more straightforward to perform. This book explains how to express data in terms of the strength of statistical evidence for competing hypotheses.The evidential approach is currently underused, despite its mathematical precision and statistical validity. Evidence-Based Statistics is an accessible and practical text filled with examples, illustrations and exercises. Additionally, the companion website complements and expands on the information contained in the book.While the evidential approach is unlikely to replace probability-based methods of statistical inference, it provides a useful addition to any statistician's 'bag of tricks.' In this book:\* It explains how to calculate statistical evidence for commonly used analyses, in a step-by-step fashion\* Analyses include: t tests, ANOVA (one-way, factorial, between- and within-participants, mixed), categorical analyses (binomial, Poisson, McNemar, rate ratio, odds ratio, data that's 'too good to be true', multi-way tables), correlation, regression and nonparametric analyses (one sample, related samples, independent samples, multiple independent samples, permutation and bootstraps)\* Equations are given for all analyses, and R statistical code provided for many of the analyses\* Sample size calculations for evidential probabilities of misleading and weak evidence are explained\* Useful techniques, like Matthews's critical prior interval, Goodman's Bayes factor, and Armitage's stopping rule are describedRecommended for undergraduate and graduate students in any field that relies heavily on statistical analysis, as well as active researchers and professionals in those fields, Evidence-Based Statistics: An Introduction to the Evidential Approach - from Likelihood Principle to Statistical Practice belongs on the bookshelf of anyone who wants to amplify and empower their approach to statistical analysis.
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In den WarenkorbZustand: New. 2020. 1st Edition. Hardcover. . . . . .
Verlag: Institute of Mathematical Statistics, 1984
ISBN 10: 0940600064 ISBN 13: 9780940600065
Sprache: Englisch
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Softcover. 206 p. Very Good condition. Reading pages are clean and without markings. Slight signs of storage and use. Spine slightly light-margined. Spine edge slightly bumped. Book corners and edges slightly bumped. Otherwise a good copy. 9780940600065 Sprache: Englisch Gewicht in Gramm: 358.
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In den WarenkorbHardcover. Zustand: Brand New. 240 pages. 9.25x6.25x1.00 inches. In Stock.
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In den Warenkorbpaperback. Zustand: New. New. book.
Verlag: Institute of Mathematical Statis, 1984
ISBN 10: 0940600064 ISBN 13: 9780940600065
Sprache: Englisch
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In den Warenkorbpaperback. Zustand: Good. Good paperback, bumped/creased with shelfwear; may have previous owner's name inside. Standard-sized.
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In den WarenkorbZustand: New. 2020. 1st Edition. Hardcover. . . . . . Books ship from the US and Ireland.