Introduction
What this guide covers
A rigorous and exhaustive breakdown of generalized linear theory, orthogonal projections on Hilbert spaces, and classical parameter estimation.
Overview
Topics covered
- Introduction
- Multiple Linear Regression Model
- Introduction and Fundamentals of the Multiple Model
- 1.1 Transition to Matrix Algebra
- 1.2 Model Geometry
- Classical Assumptions in Matrix Formulation
Related concepts
Main connections
Compatible notation
Mathematical sample in LaTeX
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Y_i = \beta_0 + \beta_1 X_{i1} + \beta_2 X_{i2} + \ldots + \beta_k X_{ik} + \epsilon_i