The Meaning of Logistic Regression: Meaning in the Model

The Meaning of Logistic Regression: Meaning in the Model
The Meaning of Logistic Regression: Meaning in the Model

Principal speaker

Associate Professor Sama Low-Choy

Logistic regression provides a great introduction to generalized linear models (GLMs); it is in fact a GLM with a binomial sampling model, so is sometimes referred to as binomial regression. We will explore both traditional (Frequentist) as well as Bayesian approaches to modelling how a binary response (also referred to as a dependent variable or an outcome) may relate to several explanatory variables (also known as independent variables or predictors).

Meaning in the Model: understanding the model helps estimate and interpret effects, regardless of the statistical paradigm you choose for inference, whether that be: operations research (least squares), classical aka Frequentist (maximum liklehood) or Bayesian (e.g. Markov Chain Monte Carlo).

Format: These 'Stat-a-along' sessions are designed so that you can replay the session slowly, whilst following similar steps with your own data.

Intended audience: Those with no experience in regression, wishing to understand the model from the outset. Those with some practical experience running these models, wishing to go to the next level, and understand the meaning of estimates.


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