Optimal Level of Significance for Regression and Other Statistical Tests

Calculates the optimal level of significance based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model. The details are covered in Kim, Jae H. and Choi, In, 2020, Choosing the Level of Significance: A Decision-Theoretic Approach, Abacus. See also Kim, Jae H., 2020, Decision-theoretic hypothesis testing: A primer with R package OptSig, The American Statistician.


Reference manual

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2.1 by Jae H. Kim, a year ago

Browse source code at https://github.com/cran/OptSig

Authors: Jae H. Kim <[email protected]>

Documentation:   PDF Manual  

GPL-2 license

Imports pwr

See at CRAN