Stats Regression#

Source: examples/stats_regression.py

Introduction#

Estimate a linear trend between prototype iteration index and novelty score for a compact design-study sample.

Technical Implementation#

  1. Define one explanatory feature (iteration count).

  2. Fit an OLS regression through the package helper.

  3. Print the serialized coefficient and fit diagnostics.

 1from __future__ import annotations
 2
 3import design_research_analysis as dran
 4
 5
 6def main() -> None:
 7    """Run and print a small linear model."""
 8    prototype_iteration = [[0.0], [1.0], [2.0], [3.0], [4.0]]
 9    novelty_score = [1.0, 3.0, 5.0, 7.0, 9.0]
10    result = dran.fit_regression(
11        prototype_iteration,
12        novelty_score,
13        feature_names=["prototype_iteration"],
14    )
15    print(result.to_dict())
16
17
18if __name__ == "__main__":
19    main()

Expected Results#

Run Command

PYTHONPATH=src python examples/stats_regression.py

Prints regression coefficients, intercept, R2, MSE, and input-shape metadata.

References#

  • docs/analysis_recipes.rst