Routine |
Mark of Introduction |
Purpose |
---|---|---|
g07aaf
Example Text Example Data |
15 | nagf_univar_ci_binomial Computes confidence interval for the parameter of a binomial distribution |
g07abf
Example Text Example Data |
15 | nagf_univar_ci_poisson Computes confidence interval for the parameter of a Poisson distribution |
g07bbf
Example Text Example Data |
15 | nagf_univar_estim_normal Computes maximum likelihood estimates for parameters of the Normal distribution from grouped and/or censored data |
g07bef
Example Text Example Data |
15 | nagf_univar_estim_weibull Computes maximum likelihood estimates for parameters of the Weibull distribution |
g07bff
Example Text Example Data |
23 | nagf_univar_estim_genpareto Estimates parameter values of the generalized Pareto distribution |
g07caf
Example Text Example Data |
15 | nagf_univar_ttest_2normal Computes -test statistic for a difference in means between two Normal populations, confidence interval |
g07daf
Example Text Example Data |
13 | nagf_univar_robust_1var_median Robust estimation, median, median absolute deviation, robust standard deviation |
g07dbf
Example Text Example Data |
13 | nagf_univar_robust_1var_mestim Robust estimation, -estimates for location and scale parameters, standard weight functions |
g07dcf
Example Text Example Data |
13 | nagf_univar_robust_1var_mestim_wgt Robust estimation, -estimates for location and scale parameters, user-defined weight functions |
g07ddf
Example Text Example Data |
14 | nagf_univar_robust_1var_trimmed Computes a trimmed and winsorized mean of a single sample with estimates of their variance |
g07eaf
Example Text Example Data |
16 | nagf_univar_robust_1var_ci Robust confidence intervals, one-sample |
g07ebf
Example Text Example Data |
16 | nagf_univar_robust_2var_ci Robust confidence intervals, two-sample |
g07gaf
Example Text Example Data |
23 | nagf_univar_outlier_peirce_1var Outlier detection using method of Peirce, raw data or single variance supplied |
g07gbf
Example Text Example Data |
23 | nagf_univar_outlier_peirce_2var Outlier detection using method of Peirce, two variances supplied |