Quantile Regression: Electricity Price Prediction

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Page 2 of 3

Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.

Linear Regression: Foundations and Applications

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

Regression: Simple and Multiple Linear

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

Linear Regression Testing

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

Basics of linear regression model

Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.

Theoretical Properties of Linear Regression Estimator

Covers the theoretical properties of the linear regression estimator, including the hat matrix and the Gauss-Markov theorem.

Linear Systems: Modeling and Identification

Covers auto-encoders, linear systems modeling, system identification, and recursive least squares.

Linear Least Squares: Minimizing Error Coefficients

Explores linear least squares, normal equations, and the importance of linear regression in minimizing errors.

Linear Regression: Fundamentals and Applications

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

Linear Regression: Basics and Principle of Least Squares

Covers linear regression basics, focusing on minimizing error using the principle of least squares and includes an ANOVA table and practical example in R.