When conducting analysis with a binary outcome, it is essential to align the interpretation of the results with the goal of the analysis, whether it’s inference or prediction. Unlike linear regression, the output of logistic regression can vary significantly depending on the purpose.

We will provide general tips for interpreting R output professionally. A typical R output is shown in the accompanying figure.

When the goal is prediction, techniques such as train-test splits within resampling frameworks like cross-validation, along with performance measures like classification error, Area Under the Curve (AUC), and others, are commonly used to evaluate model performance.

However, the focus here is on inference rather than prediction. Specifically, we aim to interpret a single model output to analyze the association between a health outcome and a defined exposure in an observational study.

While this article focuses on a specific example, the interpretation techniques discussed are generalizable and can be applied to similar datasets in other contexts.

Logistic Regression in R: Interpreting the Output

A typical logistic regression output in R might be derived using the following code:…

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The full article is available at the following link (The R output is accessible as well):

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