Aditya Mittal - About Me ▮

Multivariate Analysis: MANOVA and Discriminant Methods

Overview

Course project (STA135) applying multivariate statistical tools to analyze transportation-cost data. We test for group differences and build discriminant rules for classification.

Goals

  • Conduct MANOVA to assess simultaneous mean differences across groups.
  • Examine variable contributions and correlations.
  • Fit LDA/QDA/other discriminant models and evaluate classification performance.

Data & Code

  • Primary dataset: /projects/multivariate-analysis/data/T6-10.dat
  • Additional datasets (UCI Abalone): /projects/multivariate-analysis/data/abalone/
  • Reproducible script: /projects/multivariate-analysis/code/code.R

Deliverables

  • Report: /projects/multivariate-analysis/reports/report.pdf

Outcomes

  • MANOVA indicates significant multivariate group effects; follow-up tests identify contributing variables.
  • Discriminant analysis yields interpretable decision boundaries, with performance depending on covariance assumptions.