This course covers the essentials of linear regression, including simple and multiple linear regression with matrix notation, properties of the regression estimator, inference for regression coefficients, and metrics like mean squared error, R-squared, and adjusted R-squared. The course aims to teach the skills needed to understand and apply linear regression models. The teaching method involves theoretical explanations and practical examples. This course is intended for individuals interested in learning about linear regression and its applications in data analysis and statistics.
Overview
Syllabus
Simple Linear Regression with Matrix Notation.
Multiple Linear Regression Part I: Notation and Dimensions.
Multiple Linear Regression Part II: Properties of the Regression Estimator.
Multiple Linear Regression Part III: Inference for One Regression Coefficient.
Multiple Linear Regression Part IV: Mean Squared Error, R-squared, and Adjusted R-squared.
Taught by
Professor Knudson
