| 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 |
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