Material and process performance usually depends on many variables at once. Our multivariate data analysis uses methods such as principal component analysis, clustering and multiple regression to reveal how they interact.
This is especially valuable for spectroscopic data, quality data with many measured properties and complex process records.
Common problems we're called in to solve
- Dozens of measured properties make patterns hard to see.
- Spectroscopic data needs classification or quantification.
- Product grades or suppliers need to be grouped objectively.
- Correlated variables confuse simple analysis.
Our approach
- 1Prepare and scale multivariate data.
- 2Apply PCA, PLS, clustering or regression as appropriate.
- 3Validate models and interpret components.
- 4Translate findings into practical actions.
Outcomes and benefits
- Clear view of complex relationships.
- Objective grouping and classification.
- Better prediction of quality and performance.
Serving Pleasant Hill and the Bay Area
We help research groups and manufacturers across the Bay Area and California extract more value from complex datasets.
Frequently asked questions
What is principal component analysis?
A method that reduces many correlated variables to a few components that capture most of the variation.
When is multivariate analysis needed?
Whenever many variables are measured and their combined effect matters.
Can it be used for supplier comparison?
Yes. It is an effective way to compare suppliers across many quality properties.