Multivariate Statistical Analysis

Develop a comprehensive understanding of multivariate statistical analysis techniques through a 13-hour course that combines theoretical explanations, problem-solving, and practical implementation in R and Excel. Explore key topics including the multivariate normal distribution, hypothesis testing, linear regression models, principal component analysis, factor analysis, and clustering. Gain insights into data reduction, simplification, grouping, and prediction methods applicable to various fields such as machine learning, data science, mathematics, statistics, medicine, economics, and social sciences. Engage with video solutions, theorems, proofs, and hands-on exercises to master concepts from sample geometry and random sampling to discrimination and classification. Build a strong foundation for comprehending advanced multivariate techniques, with a prerequisite knowledge of linear algebra, probability, and statistics. Utilize suggested literature from renowned authors to supplement your learning experience.