# F1-dev-01 — Analytic firm dataset and revenue aggregates

# Question

Build a documented, analysis-ready, one-row-per-firm version of the synthetic firm cross-section data/firms.csv. Then report: the number of distinct firms; the mean and median revenue (millions USD) over firms with known revenue; and the mean head count over all distinct firms.

# Data and exclusions

The file has 445 data rows (n_input_rows). I found three issues, all listed in data_issues:

1. Duplicate rows. 45 rows are exact duplicates of another row (n_exact_duplicate_rows). I checked that no firm_id appears with conflicting values, so I kept the first copy. This leaves 400 distinct firms (n_unique_firms).
2. Mixed units. 60 distinct firms report revenue in USD_thousands (n_thousands_unit_firms); the rest report in USD_millions. I converted all revenue to millions USD.
3. Missing revenue. 6 distinct firms have an empty revenue cell (n_missing_revenue): F0048, F0121, F0129, F0269, F0348, F0399. They are dropped from the revenue aggregates and from analytic.csv, but kept for mean_employees.

The ledger in out/exclusions.csv runs 445 → 400 (duplicates) → 400 (unit conversion) → 394 (missing revenue). So n_analysed = 394, and 445 − 45 − 6 = 394.

# Method

This is a deterministic cleaning pipeline (out/analysis.py), applied in this order: drop exact duplicates; convert units (USD_thousands × 10⁻³); drop rows with missing revenue (complete cases, no imputation). The script stops with an error if it finds conflicting duplicates or an unknown unit. No randomness is involved (random_seed = null).

# Results

The naïve baseline (mean_revenue_raw) is about 120 times the cleaned mean (mean_revenue_musd). Almost all of that gap comes from leaving the USD_thousands rows unconverted. The mean is well above the median, so the revenue distribution is right-skewed.

# Uncertainty

The aggregates are descriptive statistics of the file itself, not estimates for a wider population. If the 394 firms are treated as a sample, the standard deviation of revenue is 274.30 M USD (sd_revenue_musd) and the standard error of the mean is 13.82 M USD (se_mean_revenue_musd).

# Limitations and open issues

• The data are simulated. The cleaning trusts the unit column on each row; I did not try to detect unit errors beyond that, although converted values fall in a plausible range (5.7–2057.6 M USD).
• Firms with missing revenue are excluded rather than imputed. If revenue is not missing at random, the revenue aggregates could be biased; with only 6 of 400 firms affected, the effect is small.
• mean_employees covers all 400 distinct firms, including the 6 with missing revenue, as the task specifies. It is not computed over the analytic dataset.
• The task was completed in full (feasibility = "complete").