Logistic Regression: Maximum Likelihood Estimation

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Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Explores the impact of a binary covariate on binary responses using 2x2 tables.

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Covers the properties of the exponential family and the estimation of parameters.

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

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

Covers supervised learning through likelihood maximization to find optimal parameters.

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.