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

# Question

Build a documented one-row-per-firm analytic dataset from the synthetic file data/firms.csv and report: the number of distinct firms, the mean and median revenue in millions of USD over firms with known revenue, and the mean head count over all distinct firms.

# Data and exclusions

data/firms.csv has 445 data rows (n_input_rows), 5 columns. Detected problems (data_issues):

1. Duplicate rows — 45 rows repeat an earlier row exactly (same firm_id, identical values); none conflict. Kept first occurrence, giving 400 distinct firms (n_unique_firms).
2. Mixed revenue units — 60 distinct firms report revenue in USD_thousands, the rest in USD_millions. All values were converted to millions USD (thousands ÷ 1000). Converted magnitudes match those of the millions rows, so the unit labels look correct.
3. Missing revenue — 7 raw rows, i.e. 6 distinct firms (F0048, F0121, F0129, F0269, F0348, F0399), have no revenue (n_missing_revenue). They are left out of the revenue aggregates and out/analytic.csv, but they still count toward n_unique_firms and mean_employees.

Exclusion ledger (out/exclusions.csv): 445 → (exact duplicates, −45) → 400 → (unit conversion, −0) → 400 → (missing revenue, −6) → 394 = n_analysed.

# Method

This is a deterministic descriptive computation in out/analysis.py, with no randomness (random_seed = null). The baseline mean_revenue_raw is the plain mean of the non-empty revenue values as read, before any cleaning. Mean and median revenue are computed over the 394 analytic firms in millions USD; with an even n, the median is the average of the two middle values. Mean employees is computed over all 400 distinct firms.

# Results

• Distinct firms: 400 (n_unique_firms); firms with missing revenue: 6 (n_missing_revenue); firms analysed: 394 (n_analysed).
• Mean revenue: 249.96 M USD (mean_revenue_musd = 249.9620761421).
• Median revenue: 162.306 M USD (median_revenue_musd).
• Mean employees: 1101.435 (mean_employees).
• Baseline raw mean: 30695.88 in mixed file units (mean_revenue_raw). It is not interpretable, because it mixes thousands and millions and counts duplicate rows twice. Comparing it with mean_revenue_musd shows how large the unit problem is.
• Feasibility: complete (feasibility).

# Uncertainty

These figures are exact statistics of the file as provided, not estimates of a wider population, so no sampling standard errors or confidence intervals are reported. The remaining uncertainty is about how the data were handled, not about sampling. It comes from three sources: (i) trusting the row-level unit labels; (ii) the 6 firms with missing revenue — if their revenue is not missing at random, the revenue aggregates could be biased; (iii) treating exact duplicates as assembly errors rather than as separate firms.

# Limitations and open issues

• The data are simulated. Unit labels were checked only for plausible magnitudes; mislabelled units within the plausible range cannot be detected.
• No imputation was done for missing revenue, so the revenue aggregates cover 394 of 400 firms.
• Revenue is right-skewed: the mean (249.96) is well above the median (162.306).