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Principles of Statistical Analysis: Learning from Randomized Experiments (Institute of Mathematical Statistics Textbooks) - Softcover
Synopsis
This concise course in data analysis and inference for the mathematically literate builds on survey sampling and designed experiments.
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About the Author
Ery Arias-Castro is a professor in the Department of Mathematics and in the Halıcıoğlu Data Science Institute at the University of California, San Diego, where he specializes in theoretical statistics and machine learning. His education includes a bachelor's degree in mathematics and a master's degree in artificial intelligence, both from École Normale Supérieure de Cachan (now École Normale Supérieure Paris-Saclay) in France, as well as a Ph.D. in statistics from Stanford University in the United States.
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- PublisherCambridge University Press
- Publication date2022
- ISBN 10 1108747442
- ISBN 13 9781108747448
- BindingPaperback
- LanguageEnglish
- Number of pages408
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Principles of Statistical Analysis (Institute of Mathematical Statistics Textbooks)
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Principles of Statistical Analysis : Learning from Randomized Experiments
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Principles of Statistical Analysis Principles of Statistical Analysis: Learning from Randomized Experiments
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Principles of Statistical Analysis (Institute of Mathematical Statistics Textbooks)
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Principles of Statistical Analysis (Institute of Mathematical Statistics Textbooks)
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Principles of Statistical Analysis
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Principles of Statistical Analysis: Learning from Randomized Experiments
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Principles of Statistical Analysis (Paperback)
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ISBN 10: 1108747442
ISBN 13: 9781108747448
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Paperback. Condition: new. Paperback. This compact course is written for the mathematically literate reader who wants to learn to analyze data in a principled fashion. The language of mathematics enables clear exposition that can go quite deep, quite quickly, and naturally supports an axiomatic and inductive approach to data analysis. Starting with a good grounding in probability, the reader moves to statistical inference via topics of great practical importance simulation and sampling, as well as experimental design and data collection that are typically displaced from introductory accounts. The core of the book then covers both standard methods and such advanced topics as multiple testing, meta-analysis, and causal inference. This concise course in principled data analysis for the mathematically literate uses survey sampling and designed experiments as a foundation for statistical inference. Covering essentials for advanced undergraduates and selected topics typically taught at the graduate level, its 700 problems many computational build understanding and skills. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781108747448
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