Linear Regression Essentials

Related lectures (32)

Page 3 of 4

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Explores linear regression using the method of least squares to fit data points with the equation y = ax + b.

Explores linear regression diagnostic methods, variance analysis, and experimental design concepts.

Covers multicollinearity, outliers, model specification, and practical strategies in linear regression.

Explains the decomposition of total sum of squares, model adjustment, and parameter estimation in linear regression.

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Explores least squares in linear regression, hypothesis testing, outliers, and model assumptions.

Explores linear regression analysis of ozone data using statistical models.

Covers linear regression, from developing research questions to interpreting R-squared and adding predictors to improve the model.

Covers the analysis of various data attributes and linear regression models.