Statistical Methods: Multivariate Models
A practical course using multivariate statistical techniques to gain greater insights from your analysis.
Why Does this Course Matter:
Modern organisations work with rich, complex data across customer behaviour, logistics, policy outcomes, and operations. This course helps professionals extract structure and meaning from such datasets, making it easier to segment users, simplify information, and support targeted strategies. The techniques covered are vital for robust modelling and reporting in data-driven environments.
What Will I Learn:
- Confidence in working with and preparing multivariate datasets
- Ability to select and apply a range of multivariate techniques to real problems
- Skills to identify relationships, reduce dimensionality, and classify patterns in data
- Interpretation of analysis outputs to support clearer, evidence-based decisions
- Awareness of when and why to apply techniques such as PCA, cluster, factor or discriminant analysis
Course Topics:
- Learn how to prepare real-world datasets for multivariate analysis
- Understand and apply Principal Components Analysis to simplify data
- Use Factor and Cluster Analysis to uncover hidden structures and groupings
- Apply Discriminant Analysis to predict and classify outcomes
- Explore practical examples that highlight the commercial use of each technique
- Build analytical capability that enhances insight, strategy, and modelling
Who is this course for:
- Operational Researchers who want to expand beyond basic statistical techniques
- Analysts handling datasets with many variables and looking for deeper insights
- Professionals in government, healthcare, finance, logistics, or retail working with complex data
- Data analysts and forecasters who want to apply segmentation and classification tools
- Anyone in a decision support or research role aiming to improve evidence-based recommendation
Related Courses
Prices
- OR Society Member £495 + VAT
- Non-Member £625 + VAT
- Student Member £370 + VAT
- For group booking discounts please contact [email protected]
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Registration
Last day to register is 17/11/2026