additional_features
["Built-in sampling-design detection rules and variance strategies (Taylor/stratified/SRS approximation)", "Missing-data handling (default NA + user-defined missing codes)", "Standardized output schema and rounding rules for estimates/SE/RSE/CIs"]
example_commands
["Compute SE/RSE/95% CIs for this CSV. (upload file)", "Use weight=final_weight, strata=strata_id, PSU=psu_id; treat y1 as continuous and y2 as binary.", "Treat -9, -8, and 9999 as missing; auto-select the top 30 numeric variables."]
gpt_id
g-689c6ac1e9bc81919ddebf9daed3c0e8
ideal_use_cases
["Quickly compute SE, RSE, and 95% CIs for means/proportions from complex sample surveys", "Automatically map design elements from messy CSVs with ambiguous column names", "Generate report-ready, standardized output tables (estimate, SE, RSE, CI)"]
limitations
["Variance estimates can be inaccurate if design variables (weights/strata/PSU, etc.) are missing or mapped incorrectly.", "Some designs may trigger conservative handling/warnings (e.g., only one PSU within a stratum).", "Primarily focused on means/proportions; advanced modeling (e.g., regression) may be out of scope."]
target_users
["Survey/statistics practitioners (public or private sector)", "Data analysts and academic researchers working with complex survey data", "Report authors who need design-based uncertainty (SE/RSE) for survey estimates"]