Abstract

This presentation was part of a staff workshop focused on empirical methods and applied research. This includes a basic overview of regression with matrix algebra, maximum likelihood, inference, and model assumptions. Distinctions are made between paradigms related to classical statistical methods and algorithmic approaches. The presentation concludes with a brief discussion of generalization error, data partitioning, decision trees, and neural networks.

Disciplines

Agribusiness | Agricultural and Resource Economics | Applied Statistics | Econometrics | Management Sciences and Quantitative Methods | Models and Methods