Linear Regression: Estimation and Testing

Related lectures (30)

Explores unilateral and bilateral pairs in statistical hypothesis testing, covering critical values, test statistics, and p-values.

Explores estimating parameters through confidence intervals in linear regression and statistics.

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

Covers the essentials of linear regression, focusing on using multiple quantitative explanatory variables to predict a quantitative outcome.

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

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

Explores confidence intervals, hypothesis testing, and decision-making using test statistics and p-values.

Covers the estimation of theoretical frequencies and independence testing between two characteristics, with examples for practical application.

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

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

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