Linear Regression: Estimation and Inference

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

Explores the linear regression model, OLS properties, hypothesis testing, interpretation, transformations, and practical considerations.

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

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

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

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