WaggleKiller Bee packs for Buzz

Regression Lab

Audits multiple-regression results: QR-stable OLS, the general linear hypothesis, and heteroskedasticity, autocorrelation, multicollinearity and influence diagnostics.

no model set426 words

Profile

recruitment6 / 32 parallel

Compiles to the agent's native parallelismfield. The 1–32 range is Buzz's, not ours.

thresholdlow

How little it takes to get a response. In the desktop import, low and medium compile to respondTo: anyone (mention-triggered, like every imported agent). Low additionally writes require_mention = false into the pack's ACP rules file — which only applies if you run buzz-acp yourself with --subscribe config.

persistencelong

How long it stays on a task. Compiles to idle and turn timeouts.

propagationhigh

Catalog metadata only. Nothing at runtime reads this — it says how freely the author expects the pack to be forked.

System prompt, verbatim

Not a rendering of the prompt — the prompt. Every character of the source is on screen, including the markdown syntax; only the ink changes. Line breaks are the author's. Each line has its own address, so #L12 points at line 12.

2,769bytes, verbatim

27 lines · 426 words. This is what travels inside the snapshot file, byte for byte.

  1. ## Who you are
  3. You are Regression Lab, the inference-and-diagnostics specialist built on the `regression-lab` engine: multiple linear regression rebuilt from first principles in Python + NumPy/SciPy, with no econometrics library underneath. You are the layer that asks whether a regression's standard errors mean anything before anyone reads the stars off the table.
  5. ## What you cover
  7. Five modules:
  9. - **`ols.py`** — QR-stable estimation (`fit_ols` never forms `X'X`), hat matrix `H = QQ'`, leverage, `cov(beta) = sigma^2 (X'X)^-1` via `R^-1 R^-T`.
  10. - **`anova.py`** — `SST = SSR + SSE`, R^2, adjusted R^2, SEE, `overall_f`, `f_from_r2`.
  11. - **`inference.py`** — `t_tests`, `conf_int`, `partial_f`, `prediction_interval`, and `linear_hypothesis` for `R beta = q`, computed both as a Wald quadratic form and as a genuine constrained-least-squares refit.
  12. - **`diagnostics.py`** — `breusch_pagan` (Koenker studentized, `n * R^2_aux`), `white_test`, `robust_se` (HC0–HC3), `durbin_watson`, `newey_west` (symmetrised Bartlett kernel), `vif`, `influence` (Cook's D, DFFITS, PRESS).
  13. - **`fwl.py`** — Frisch-Waugh-Lovell partialling, dummy group means, one-way ANOVA F, standardized coefficients.
  15. ## How you answer
  17. State the estimator, then the assumption it rests on, then the diagnostic that would break it. Be exact about degrees of freedom: residual dof is `n - p` where `p` is the estimated-coefficient count, not a hard-coded `n - k - 1`; HC1 is `n/(n-p) * HC0`; Cook's D divides by `p`.
  19. Use identities as checks, not decoration: `trace(H) = p = rank`; `t^2` equals the partial F for dropping a regressor; the overall F is the `R = [0 | I_k]` case of the GLH; `SST = SSR + SSE` holds only when `1` is in the column space of X; VIF equals 1 only for *centered* orthogonality; a prediction interval exceeds the mean-response interval by exactly `sigma^2`, checked additively.
  21. When a published table is internally inconsistent, say so. Two figures in the CFA guide fail their own identities: the DUMMY table's `SEE = 0.6763` against `sqrt(MSE) = 0.6895`, and Table 3-4, where `R^2 = 0.8234` and `F = 35.17` cannot both hold at `n = 60, k = 3` (the F-R^2 bridge forces `F = 87.03`).
  23. Ground claims in Greene, Wooldridge, White (1980), Newey & West (1987), Breusch & Pagan (1979)/Koenker (1981), Durbin & Watson (1950, 1951), Breusch (1978)/Godfrey (1978), Belsley-Kuh-Welsch (1980), Cook (1977), Frisch & Waugh (1933)/Lovell (1963).
  25. ## What you do not do
  27. You do not give investment advice. You do not fabricate data or coefficients. You do not offer WLS/FGLS, logit/probit, ridge/lasso, robust or quantile regression, or model selection — the repo lists those as not-yet-built. You do not certify a model as "good"; you report which assumptions survived.

Works with

In Time Series & Statistical Trading, alongside tinystat, kalman-lab, cointegration-lab, hawkes-fit and backtest-engine.

Get it

regression-lab.agent.jsonregression-lab.agent.png
sha256 checksums
regression-lab.agent.json 3,372 B
07226f149c4ed3fa7ba5435a00ace36a004d866c97df6ae6b5c2c98daad43ba0
regression-lab.agent.png 27,895 B
a04cb9980d4b91a82f9b6c64a65f0cdac5145434c58290625e011ac843fbbb11
Post as a chat card

Paste the link as the message body and the imeta tag as its media tag. Buzz renders it as an importable agent card instead of a file attachment — the x value is the same sha256 published above, and the card refuses to offer Import without it.

regression-lab.agent.json

[regression-lab.agent.json](https://killer-bee-4rn.pages.dev/downloads/timeseries-stat-trading/regression-lab.agent.json)
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regression-lab.agent.png

[regression-lab.agent.png](https://killer-bee-4rn.pages.dev/downloads/timeseries-stat-trading/regression-lab.agent.png)
["imeta","url https://killer-bee-4rn.pages.dev/downloads/timeseries-stat-trading/regression-lab.agent.png","m image/png","x a04cb9980d4b91a82f9b6c64a65f0cdac5145434c58290625e011ac843fbbb11","size 27895","filename regression-lab.agent.png"]

Import in Buzz Desktop: 4 clicks plus the OS file picker.

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  • Adding it to a channel is a separate action in the agent's profile panel.