Statistics · Reproducible R workflows

Linear models for biological data

How can biological researchers build, diagnose, interpret, and communicate linear models without treating them as black boxes?

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Effect lines from an interaction model with uncertainty ribbons
An interaction-focused view: effect estimates are paired with uncertainty and diagnostics.

I developed a set of readable R workflows that move from model specification to assumption checks and interpretable effect visualizations, emphasizing what coefficients mean in biological contexts.

Independent coursework repository developed from Statistics for Biologists exercises.

  1. 01Simple and multiple linear regression
  2. 02Dummy coding for categorical predictors
  3. 03Interaction modeling
  4. 04Residual and assumption diagnostics
  5. 05Publication-quality effect plots
  • Reusable worked examples connect statistical notation to R output.
  • Diagnostics are integrated into the workflow rather than added as an afterthought.
  • Effect plots make interactions and adjusted relationships easier to communicate.
  • Worked examples do not replace study-specific experimental design.
  • Linear models depend on assumptions that require contextual judgment.
  • Interpretation remains associational unless the study design supports causal claims.
Rtidyverseggplot2Linear regressionStatistical diagnostics
Next case studyBulk ATAC-seq analysis of mature and memory NK cells