Randomization-Based Statistical Inference: A Resampling and Simulation Infrastructure
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ERIC Institute of Education Sciences
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| Main Authors: | , , , |
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| Format: | Recurso educativo Open Access |
| Language: | en |
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2018
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| _version_ | 1867181621024653312 |
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| author | Dinov, Ivo D. Palanimalai, Selvam Khare, Ashwini Christou, Nicolas |
| author_facet | Dinov, Ivo D. Palanimalai, Selvam Khare, Ashwini Christou, Nicolas Dinov, Ivo D. Palanimalai, Selvam Khare, Ashwini Christou, Nicolas |
| collection | Education Resources Information Center |
| contents | Randomization-Based Statistical Inference: A Resampling and Simulation Infrastructure Dinov, Ivo D. Palanimalai, Selvam Khare, Ashwini Christou, Nicolas Statistical Inference Sampling Simulation Computer Oriented Programs Experiments Observation Teaching Methods Scientific Concepts Statistical inference involves drawing scientifically-based conclusions describing natural processes or observable phenomena from datasets with intrinsic random variation. We designed, implemented, and validated a new portable randomization-based statistical inference infrastructure (http://socr.umich.edu/HTML5/Resampling_Webapp) that blends research-driven data analytics and interactive learning, and provides a backend computational library for managing large amounts of simulated or user-provided data. We designed, implemented and validated a new portable randomization-based statistical inference infrastructure (http://socr.umich.edu/HTML5/Resampling_Webapp) that blends research-driven data analytics and interactive learning, and provides a backend computational library for managing large amounts of simulated or user-provided data. The core of this framework is a modern randomization webapp, which may be invoked on any device supporting a JavaScript-enabled web browser. We demonstrate the use of these resources to analyse proportion, mean and other statistics using simulated (virtual experiments) and observed (e.g. Acute Myocardial Infarction, Job Rankings) data. Finally, we draw parallels between parametric inference methods and their distribution-free alternatives. The Randomization and Resampling webapp can be used for data analytics, as well as for formal, in-class and informal, out-of-the-classroom learning and teaching of different scientific concepts. Such concepts include sampling, random variation, computational statistical inference and data-driven analytics. The entire scientific community may utilize, test, expand, modify or embed these resources (data, source-code, learning activity, webapp) without any restrictions. |
| format | Recurso educativo Open Access |
| id | eric_EJ1175750 |
| institution | ERIC Institute of Education Sciences |
| language | en |
| publishDate | 2018 |
| record_format | eric |
| spellingShingle | Randomization-Based Statistical Inference: A Resampling and Simulation Infrastructure Dinov, Ivo D. Palanimalai, Selvam Khare, Ashwini Christou, Nicolas Statistical Inference Sampling Simulation Computer Oriented Programs Experiments Observation Teaching Methods Scientific Concepts Randomization-Based Statistical Inference: A Resampling and Simulation Infrastructure Dinov, Ivo D. Palanimalai, Selvam Khare, Ashwini Christou, Nicolas Statistical Inference Sampling Simulation Computer Oriented Programs Experiments Observation Teaching Methods Scientific Concepts Statistical inference involves drawing scientifically-based conclusions describing natural processes or observable phenomena from datasets with intrinsic random variation. We designed, implemented, and validated a new portable randomization-based statistical inference infrastructure (http://socr.umich.edu/HTML5/Resampling_Webapp) that blends research-driven data analytics and interactive learning, and provides a backend computational library for managing large amounts of simulated or user-provided data. We designed, implemented and validated a new portable randomization-based statistical inference infrastructure (http://socr.umich.edu/HTML5/Resampling_Webapp) that blends research-driven data analytics and interactive learning, and provides a backend computational library for managing large amounts of simulated or user-provided data. The core of this framework is a modern randomization webapp, which may be invoked on any device supporting a JavaScript-enabled web browser. We demonstrate the use of these resources to analyse proportion, mean and other statistics using simulated (virtual experiments) and observed (e.g. Acute Myocardial Infarction, Job Rankings) data. Finally, we draw parallels between parametric inference methods and their distribution-free alternatives. The Randomization and Resampling webapp can be used for data analytics, as well as for formal, in-class and informal, out-of-the-classroom learning and teaching of different scientific concepts. Such concepts include sampling, random variation, computational statistical inference and data-driven analytics. The entire scientific community may utilize, test, expand, modify or embed these resources (data, source-code, learning activity, webapp) without any restrictions. |
| title | Randomization-Based Statistical Inference: A Resampling and Simulation Infrastructure |
| topic | Statistical Inference Sampling Simulation Computer Oriented Programs Experiments Observation Teaching Methods Scientific Concepts |
| url | https://eric.ed.gov/?id=EJ1175750 |