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A reproducible matplotlib figure for the new "Fixed and random block effects" material in the pid-book Design and Analysis of Experiments chapter. It shows a blocked experiment's two variance components: the block-to-block scatter of the block means about the grand mean (sigma_gamma, the random block effect) and the within-block scatter of the runs about their block mean (sigma_eps, the residual). Values are hard-coded so the figure is deterministic; run from this directory to write the PNG alongside it. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01TH8TdNx3iJmWqvaoZz5mQx
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What this adds
A reproducible matplotlib figure (script + PNG in
doe/) for the new Fixed and random block effects material in the pid-book Design and Analysis of Experiments chapter:blocking-variance-components: a blocked experiment shown as runs grouped by block (day), with each block's mean offset from the grand mean. It labels the two variance components: the block-to-block scatter of the block means (σ_γ, the random block effect) and the within-block scatter of the runs about their block mean (σ_ε, the residual). This visualizes the intraclass correlationρ = σ_γ² / (σ_γ² + σ_ε²).Follows the existing
doe/*.pyconvention (standalone matplotlib,savefig(..., dpi=300, transparent=True), deterministic hard-coded values, run from its own directory).Paired PR
The pid-book side that embeds this is kgdunn/pid-book# (linked from that PR). Merge this figures PR first (or together) so the pid-book PDF build can read the PNG.
🤖 Generated with Claude Code
https://claude.ai/code/session_01TH8TdNx3iJmWqvaoZz5mQx
Generated by Claude Code