Learn how to build a flash-card style question bank using Google Sheets as storage, R’s Plumber API, and host it on a Digital Ocean droplet—step-by-step setup, deployment, and tips.
Enjoyed learning penalized GAM math. Built penalty matrices, optimized λ using GCV, and implement our own GAM function. Confusing? Yes! Rewarding? Oh yes!
I finally understood B-splines by working through the Cox-deBoor algorithm step-by-step, discovering they’re just weighted combinations of basis functions that make non-linear regression linear. What surprised me is going through Bayesian statistics really helped me understand the engine behind the model! Will try this again in the future!
We learnt to derive the Newton-Raphson algorithm from Taylor series approximation and implements it for logistic regression in R. We’ll show how the second-order Taylor expansion leads to the Newton-Raphson update formula, then compare individual parameter updates versus using the full Fisher Information matrix for faster convergence
Refreshed my rusty calculus skills lately! 🤓 Finally understand what happens during complete separation and why those coefficient SE get so extreme. The math behind maximum likelihood estimation makes more sense now! Chain rule, quotient rule, matrix inversion are crucial!