Assessing Significance and Fit

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

Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.

Explores statistical inference for linear models, covering model fitting, parameter estimation, and variance decomposition.

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Explores logistic regression for binary response variables, covering topics such as odds ratio interpretation and model fitting.

Introduces linear models, regression, Gaussian distribution, linearity, and model generalization.

Explores linear regression analysis of ozone data using statistical models.

Introduces simple and multiple normal linear regression, and maximum likelihood estimation with practical examples.

Introduces probability, estimation methods, linear models, testing, and advanced regression techniques.

Explores the distribution theory of least squares estimators in a Gaussian linear model.