000			02087nam a2200361 i 4500
001			CR9781108779197
003			UkCbUP
005			20240916194638.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			190522s2022||||enk o ||1 0|eng|d
020			_a9781108779197 (ebook)
020			_z9781108489676 (hardback)
020			_z9781108747448 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA276.12 _b.A75 2022
082	0	0	_a519.5 _223/eng20220722
100	1		_aArias-Castro, Ery, _eauthor.
245	1	0	_aPrinciples of statistical analysis : _blearning from randomized experiments / _cEry Arias-Castro, University of California, San Diego.
264		1	_aCambridge, United Kingdom ; _aNew York, NY : _bCambridge University Press, _c2022.
300			_a1 online resource (xvii, 389 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aInstitute of Mathematical Statistics textbooks ; _v15
500			_aTitle from publisher's bibliographic system (viewed on 27 Jul 2022).
520			_aThis 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.
650		0	_aMathematical statistics _vTextbooks.
776	0	8	_iPrint version: _z9781108489676
830		0	_aInstitute of Mathematical Statistics textbooks ; _v15.
856	4	0	_uhttps://doi.org/10.1017/9781108779197
942			_2ddc _cEB
999			_c9519 _d9519