Inference: Poisson Regression

Related lectures (31)

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

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

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

Covers iterative weighted least squares, Poisson regression, mixed models, and likelihood ratio statistic.

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

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

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

Covers linear models, binary classification, logistic regression, and model evaluation metrics.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.