This article puts the hypothesized causal DAG from Part 1 to the test by fitting various Multiple Linear Regression models to ~4,000 large BC wildfires (>100 hectares) and examining whether the atmospheric relationships we drew actually hold up in the data. Before the main analysis, we take a pedagogical detour through Bayesian Multiple Linear Regression using PyMC to demonstrate what happens when weakly informative priors meet a large dataset and why that finding justifies switching to simpler Frequentist methods for the rest of the analysis. The honest result: Our final model explains roughly 11% of variance in fire size, which sounds underwhelming until you consider what that 11% actually represents. Two of our hypothesized mediators don’t survive contact with the data, and our revised DAG is far better for it.

Topics covered:

• Bayesian Multiple Linear Regression with PyMC along with other Bayesian techniques like prior specification, prior predictive simulation, and posterior estimation
• Forest plots and posterior predictive plots for Bayesian model evaluation
• Correlation matrix analysis for detecting multicollinearity among atmospheric predictors
• Pooled vs. zone-stratified regression and why separating BC fire zones nearly doubles model performance (R² = 0.067 → 0.117)
• Standardized coefficient heatmaps for comparing predictor effects across geographic zones
• Using multiple lines of evidence (correlation, coefficient instability, R² comparison) to identify redundant mediators without formal VIF testing

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