# NAG FL InterfaceG07 (Univar)Univariate Estimation

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G07 (Univar) Chapter Introduction – A description of the Chapter and an overview of the algorithms available.

Routine
Mark of
Introduction

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