Description

Book Synopsis

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

Statistics by Simulation

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    £999.99

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    A Hardback by Aaron Ellison

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      View other formats and editions of Statistics by Simulation by Aaron Ellison

      Publisher: Princeton University Press
      Publication Date: 6/3/2025
      ISBN13: 9780691273891, 978-0691273891
      ISBN10: 0691273898

      Description

      Book Synopsis

      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

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