Validation of Visual Statistical Inference, Applied to Linear Models

Autor:	Dianne Cook, Heike Hofmann, Mahbubul Majumder
Rok vydání:	2013
Předmět:	Statistics and Probability Computer science Linear model Nonparametric statistics Inference computer.software_genre Visualization Exploratory data analysis Statistical inference Data mining Statistics Probability and Uncertainty computer Statistical graphics Statistical hypothesis testing
Zdroj:	Journal of the American Statistical Association. 108:942-956
ISSN:	1537-274X 0162-1459
DOI:	10.1080/01621459.2013.808157
Popis:	Statistical graphics play a crucial role in exploratory data analysis, model checking, and diagnosis. The lineup protocol enables statistical significance testing of visual findings, bridging the gulf between exploratory and inferential statistics. In this article, inferential methods for statistical graphics are developed further by refining the terminology of visual inference and framing the lineup protocol in a context that allows direct comparison with conventional tests in scenarios when a conventional test exists. This framework is used to compare the performance of the lineup protocol against conventional statistical testing in the scenario of fitting linear models. A human subjects experiment is conducted using simulated data to provide controlled conditions. Results suggest that the lineup protocol performs comparably with the conventional tests, and expectedly outperforms them when data are contaminated, a scenario where assumptions required for performing a conventional test are violated. Surpr...
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b8cbd5dfeedfb67625004ed55aeec855 https://doi.org/10.1080/01621459.2013.808157 Zobrazit plný text záznamu