Fit a regression and check expected value

Fit a trend line to a metric over time, compute the expected value of an outcome, then check its probability under a normal model.

Workflow

Step 1 of 3

Linear Regression (least squares)

Fit a least-squares trend line to a metric tracked over time.

regression({"x": [], "y": []})

Step 2 of 3

Expected Value & Variance

Compute expected value and variance for a discrete outcome.

expected_value({"outcomes": [], "probabilities": []})

Step 3 of 3

Normal Distribution Probability

Check the probability of that outcome under a normal distribution.

normal_prob({"mean": 100, "std_dev": 100, "x": 100})

Install

One command wires up every tool below for Claude Code:

claude mcp add --transport http wingman https://wingmanprotocol.com/mcp

Claude Desktop, Cursor and Continue configs are on /install. A free key (500 calls/month, no card) is on /pricing; beyond that it is x402 pay-per-call in USDC on Base.