Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Lecture
ANOVA: Model Coefficients and Sequential Sum of Squares
Graph Chatbot
Related lectures (29)
Previous
Page 1 of 3
Next
Linear Regression: Basics and Estimation
Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.
Linear Regression: Statistical Inference and Regularization
Covers the probabilistic model for linear regression and the importance of regularization techniques.
Probabilistic Models for Linear Regression
Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.
Regression: Simple and Multiple Linear
Covers simple and multiple linear regression, including least squares estimation and model diagnostics.
Probabilistic Linear Regression
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Linear Regression: Statistical Inference Perspective
Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.
Linear Regression: Estimation and Inference
Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.
Linear Regression Basics
Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Supervised Learning: Linear Regression
Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.
Linear Regression Essentials
Covers the essentials of linear regression, focusing on using multiple quantitative explanatory variables to predict a quantitative outcome.