Parameter estimation

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

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

Introduces linear regression concepts, from X-bands creation to slope estimator properties and tests.

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Explores linear regression and the least squares method to minimize the loss function for the best fit.

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Covers the basics of linear regression, focusing on estimation and prediction.