Introduction to Linear & Logistic Regression & K-Means clustering

with Amy Makawana, Nottingham University Hospitals NHS Trust

This session will be useful for anyone who would like an introduction to some of the most commonly used statistical and machine learning techniques for understanding relationships within data and making predictions. We will cover the assumptions, strengths and limitations of each method, and discuss when they are appropriate to use. The session will cover:

• the principles of linear regression and how it can be used to model relationships between variables
• logistic regression for classification problems and binary outcomes
• interpreting model outputs, coefficients and measures of model performance
• key assumptions of regression models and how to assess them
• the principles of K-means clustering and unsupervised learning
• how to identify and interpret clusters within data
• examples of applying these techniques to real-world datasets

Due to time, there will not be a practical demonstration in the session, but training materials will be shared and will include a R tutorial that you can work through in your own time.

FREE