000			03936cam a22002778i 4500
003			RNL
005			20260318092154.0
008			241129s2025 nju b 001 0 eng
020			_a9780691258775
040			_aRCL
082	0	0	_a310 D60S
100	1		_aDormann, Carsten _930767
245	1	0	_aStatistics by Simulation : _bA Synthetic Data Approach / _cCarsten F. Dormann and Aaron M. Ellison.
260			_aOxford: _bPrinceton University Press, _c2025.
300			_axv, 437p. ; 25cm.
504			_aIncludes bibliographical references and index.
505	0		_aI Propositi: Why and how to simulate -- General introduction -- What are simulated data? -- Simulated data are specific.
520			_a"An accessible guide to understanding statistics using simulations, with examples from a range of scientific disciplines Real-world challenges such as small sample sizes, skewed distributions of data, biased sampling designs, and more predictors than data points are pushing the limits of classical statistical analysis. This textbook provides a new tool for the statistical toolkit: data simulations. It shows that using simulation and data-generating models is an excellent way to validate statistical reasoning and to augment study design and statistical analysis with planning and visualization. Although data simulations are not new to professional statisticians, Statistics by Simulation makes the approach accessible to a broader audience, with examples from many fields. It introduces the reasoning behind data simulation and then shows how to apply it in planning experiments or observational studies, developing analytical workflows, deploying model diagnostics, and developing new indices and statistical methods. - Covers all steps of statistical practice, from planning projects to post-hoc analysis and model checking - Provides examples from disciplines including sociology, psychology, ecology, economics, physics, and medicine - Includes R code for all examples, with data and code freely available online - Offers bullet-point outlines and summaries of each chapter - Minimizes the use of jargon and requires only basic statistical background and skills"-- Provided by publisher.
520			_a"An accessible guide to understanding statistics using simulations, with examples from a range of scientific disciplinesReal-world challenges such as small sample sizes, skewed distributions of data, biased sampling designs, and more predictors than data points are pushing the limits of classical statistical analysis. This textbook provides a new tool for the statistical toolkit: data simulations. It shows that using simulation and data-generating models is an excellent way to validate statistical reasoning and to augment study design and statistical analysis with planning and visualization. Although data simulations are not new to professional statisticians, Statistics by Simulation makes the approach accessible to a broader audience, with examples from many fields. It introduces the reasoning behind data simulation and then shows how to apply it in planning experiments or observational studies, developing analytical workflows, deploying model diagnostics, and developing new indices and statistical methods. Covers all steps of statistical practice, from planning projects to post-hoc analysis and model checking Provides examples from disciplines including sociology, psychology, ecology, economics, physics, and medicine Includes R code for all examples, with data and code freely available online Offers bullet-point outlines and summaries of each chapter Minimizes the use of jargon and requires only basic statistical background and skills"-- Provided by publisher.
546			_aEnglish
650		0	_aMathematical statistics _930768
650		7	_aSCIENCE / Research & Methodology _930769
650		7	_aCOMPUTERS / Computer Simulation _930770
700	1		_aEllison, Aaron M., _930771
942			_cBK
999			_c47963 _d47963