Linear modeling

By the end, you’ll be able to:

  • Fit robust predictive models for both regression and classification tasks.
  • Judge feature contributions via confidence intervals, t‑tests, and F‑tests.
  • Select the best subset of predictors for optimal performance.
  • Simple vs. Multiple Linear Regression
  • Key Assumptions (Gauss–Markov Theorem)
  • Inference & Statistical Testing
    • t‑tests, F‑tests, Confidence Intervals
  • Residual & Diagnostic Analysis
  • Categorical Variables
    • Dummy Encoding, Polynomial Regression
  • Generalized Linear Models
    • Logistic Regression, Link Functions
ntroduction & Background