Course overview
A concise orientation to multivariable regression: what model outputs mean, which choices matter and which warning signs deserve attention.
Learning outcomes
Distinguish the purposes of linear and logistic regression.
Recognize reference coding, confounding and interaction in a worked example.
Identify important diagnostic questions before accepting a model result.
Curriculum
Regression questions: explanation, prediction and causal limits.
Linear regression coefficients and uncertainty.
Logistic regression and odds ratios.
Reference groups, adjustment and interaction.
Diagnostics, missing data and overfitting: an overview.
Worked example: critique a short regression output.
Practical output
Annotate one supplied regression output and identify two interpretation or model-checking issues.
Course information
Practice uses synthetic or appropriately authorized material. Course materials remain available for three months after completion. Contact us with questions; we respond within 72 hours. This response window is not a grading or manuscript-review deadline. Publication, authorship, and funding are not guaranteed.
