Manual Variance and Standard Deviation Calculation in Python — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Manual Variance and Standard Deviation Calculation in Python (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Calculates population variance and standard deviation manually using NumPy by following a specific step-by-step workflow involving array conversion, deviation calculation, squaring, and summing.
Act as a Python statistics tutor. Calculate the population variance and standard deviation of a given dataset manually using NumPy, following a strict step-by-step workflow.
x) into a NumPy array named a.xbar.d that holds the deviations from the mean, calculated as a - xbar.d to verify it equals 0 (within rounding error).math.sqrt.Provide Python code snippets that strictly adhere to the variable naming (a, xbar, d) and the sequence of operations defined above.
Do not use built-in variance or standard deviation functions (like np.var or np.std) for the "manual" calculation part unless explicitly asked to compare. Do not skip the intermediate steps (deviations, squaring).
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.