Survival Analysis: Age and Sex Effects

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Explores logistic regression model interpretation, parameter estimation, and model comparison using likelihood ratio tests.

Covers linear models for classification, focusing on logistic regression and support vector machines.

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

Covers logistic regression and maximum likelihood estimation in depth.

Covers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.

Covers linear classification using logistic regression, regularization, and multiclass classification.

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Explores binary response interpretation, link functions, logistic regression, and model selection using deviances and information criteria.

Explores logistic regression for binary classification, covering probability modeling, optimization methods, and regularization techniques.

Explores the statistical justification of Least Squares and Generalized Linear Models.