Optimal Level of Significance for Regression and Other Statistical Tests


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Documentation for package ‘OptSig’ version 1.0

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OptSig-package Optimal Level of Significance for Regression and Other Statistical Tests
data1 Data for the U.S. production function estimation
Opt.Sig Optimal Significance Level for an F-test
Opt.sig.2p.test Optimal significance level calculation for the test for two proportions (same sample sizes)
Opt.sig.2p2n.test Optimal significance level calculation for the test for two proportions (different sample sizes)
Opt.sig.anova.test Optimal significance level calculation for balanced one-way analysis of variance tests
Opt.Sig.Chisq Optimal Significance Level for a Chi-square test
Opt.sig.chisq.test Optimal significance level calculation for chi-squared tests
Opt.sig.norm.test Optimal significance level calculation for the mean of a normal distribution (known variance)
Opt.sig.p.test Optimal significance level calculation for proportion tests (one sample)
Opt.sig.r.test Optimal significance level calculation for correlation test
Opt.sig.t.test Optimal significance level calculation for t-tests of means (one sample, two samples and paired samples)
Opt.sig.t2n.test Optimal significance level calculation for two samples (different sizes) t-tests of means
Opt.SigBoot Optimal Significance Level for the F-test using the bootstrap
Opt.SigBootWeight Weighted Optimal Significance Level for the F-test based on the bootstrap
Opt.SigWeight Weighted Optimal Significance Level for the F-test based on the assumption of normality in the error term
OptSig Optimal Level of Significance for Regression and Other Statistical Tests
Power.Chisq Function to calculate the power of a Chi-square test
Power.F Function to calculate the power of an F-test
R.OLS Restricted OLS estimation and F-test