Basics of linear regression model

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

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

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

Explores linear regression analysis of ozone data using statistical models.

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

Introduces the basics of linear regression, covering OLS approach, residuals, hat matrix, and Gauss-Markov assumptions.

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

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