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Principles of statistical analysis : learning from randomized experiments / Ery Arias-Castro, University of California, San Diego.

By: Material type: TextTextSeries: Institute of Mathematical Statistics textbooks ; 15.Publisher: Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2022Description: 1 online resource (xvii, 389 pages) : digital, PDF file(s)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781108779197 (ebook)
Subject(s): Additional physical formats: Print version: : No titleDDC classification:
  • 519.5 23/eng20220722
LOC classification:
  • QA276.12 .A75 2022
Online resources: Summary: 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.
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Item type Current library Collection Call number Status Date due Barcode
eBooks eBooks Central Library Statistics & Probability Available EB0857

Title from publisher's bibliographic system (viewed on 27 Jul 2022).

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.

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