Doing Logistic Regression: Effects and Significance
Doing Logistic Regression: Effects and Significance

Principal speaker

Associate Professor Sama Low-Choy

Presuming that you understand the purpose and The Meaning of Logistic regression (3 episodes: model, explainability, predictions), this series of 4 episodes walks you through the process of doing logistic regression. However, this is *not* a software-based training - we refer to the kinds of outputs that you may obtain from any statistical package and/or virtual laboratory that can conduct logistic regression (e.g. R, Stata, SPSS, Statistica, SAS, etc). In the context of logistic regression, we show the benefits of thinking about what you are doing, and conversely, the dangers of not thinking whilst doing.

Effects and Significance: A well-entrenched way of learning about regression relies on hypothesis testing (of whether the data are consistent with an effect existing or not) and statistical significance (via p-values). Here we revisit the interpretation (and its limitations) for significance and p-values. Current best practices mandate that significance is only presented together with other findings, such as confidence or credible intervals.

Format: These 4 'Stat-a-along' walk through thinking whilst doing logistic regression

Intended audience: Those with no experience in regression, wishing to walk through the process of doing logistic regression, whilst still thinking about the outputs and what they mean. Those with some conceptual understanding of what logistic regression means, wishing to go to the next level, and put this into practice.

Preparation: We strongly suggest that you revise the concept of a function and of logarithms, revising topics of high school mathematics, presented sometime during Grades 9-11, e.g. refer to the friendly You-tubes of Mr Wu, an Australian maths teacher who won national acclaim.


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