R: Optimal Level of Significance for Regression and Other Statistical Tests

# Optimal Level of Significance for Regression and Other Statistical Tests

## Documentation for package ‘OptSig’ version 2.2

- DESCRIPTION file

## Help Pages

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