MARC details
| 000 -LEADER |
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| fixed length control field |
03936cam a22002778i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
RNL |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20260318092154.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
241129s2025 nju b 001 0 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| ISBN |
9780691258775 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
RCL |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
310 D60S |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Dormann, Carsten |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Statistics by Simulation : |
| Sub Title |
A Synthetic Data Approach / |
| Statement of responsibility, etc |
Carsten F. Dormann and Aaron M. Ellison. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication |
Oxford: |
| Name of publisher |
Princeton University Press, |
| Year of publication |
2025. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Number of Pages |
xv, 437p. ; 25cm. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
I Propositi: Why and how to simulate -- General introduction -- What are simulated data? -- Simulated data are specific. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
"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 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
"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 ## - LANGUAGE NOTE |
|---|
| Language note |
English |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical Term |
Mathematical statistics |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical Term |
SCIENCE / Research & Methodology |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical Term |
COMPUTERS / Computer Simulation |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ellison, Aaron M., |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Books |
