stats

Generate a publication-ready Table 1 by automating descriptive statistics like N, mean, SD, min, and max for key variables. This skill formats dataset summaries directly into tables suitable for manuscripts and initial variable exploration.

Yes, you can create balance tables with standardized differences for treatment vs control groups. This skill automates treatment-control balance assessments to evaluate group comparability in empirical research datasets.

This skill supports cross-language workflows for Python, R, and Stata. It applies to data analysis in economics and social science research, standardizing outputs for descriptive statistics across these platforms.

Produce a correlation matrix with significance indicators by applying this skill to your empirical dataset. It automates exploratory analysis outputs, including standardized tables and formatting-ready results for research workflows.

The best way to summarize missing data is using automated missing-data summaries and basic diagnostics. This skill assesses data quality by identifying gaps in empirical datasets before conducting econometrics regression analysis.

No specific dependencies are required to output formatting-ready descriptive statistics. This skill operates independently to generate standardized tables, missing-data summaries, and formatting-ready results from your empirical datasets.