statistical-modeling
Guided statistical modeling for user-provided sports data: selecting models for binary, continuous, and count outcomes; assumption checks; effect sizes; time-aware inference; GLM diagnostics; and complete reporting.
- Version
- 0.12.0
- License
- MIT
Pinned to revision 009f9c6daccf, so it is the text this page describes rather than whatever the author pushed since.
Files
- skills/statistical-modeling/SKILL.md
- skills/statistical-modeling/references/assumptions_and_diagnostics.md
- skills/statistical-modeling/references/bayesian_statistics.md
- skills/statistical-modeling/references/diagnostics_checklist.md
- skills/statistical-modeling/references/effect_sizes_and_power.md
- skills/statistical-modeling/references/reporting_standards.md
- skills/statistical-modeling/references/sports_glm_guide.md
- skills/statistical-modeling/references/test_selection_guide.md
- skills/statistical-modeling/scripts/assumption_checks.py
- skills/statistical-modeling/scripts/glm_diagnostics.py
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