Applied Multivariate Analysis with R

Applied Multivariate Analysis with R

Applied Multivariate Analysis (MVA) with R is a practical, conceptual and applied "hands-on" course that teaches students how to perform various specific MVA tasks using real data sets and R software. It is an excellent and practical background course for anyone engaged with educational or professional tasks and responsibilities in the fields of data mining or predictive analytics, statistical or quantitative modeling (including linear, GLM and/or non-linear modeling, covariance-based Structural Equation Modeling (SEM) specification and estimation, and/or variance-based PLS Path Model specification and estimation. Students learn all about the nature of multivariate data and multivariate analysis. Students specifically learn how to create and estimate: covariance and correlation matrices; Principal Components Analyses (PCA); Multidimensional Scaling (MDS); Cluster Analysis; Exploratory Factor Analyses (EFA); and SEM model estimation. The course also teaches how to create dozens of different dazzling 2D and 3D multivariate data visualizations using R software. All software, R scripts, datasets and slides used in all lectures are provided in the course materials. The course is structured as a series of seven sections, each addressing a specific MVA topic and each section culminating with one or more "hands-on" exercises for the students to complete before proceeding to reinforce learning the presented MVA concepts and skills. The course is an excellent vehicle to acquire "real-world" predictive analytics skills that are in high demand today in the workplace. The course is also a fertile source of relevant skills and knowledge for graduate students and faculty who are required to analyze and interpret research data.

Learn to use R software to conduct PCAs, MDSs, cluster analyses, EFAs and to estimate SEM models.

Url: View Details

What you will learn

• Conceptualize and apply multivariate skills and "hands-on" techniques using R software in analyzing real data.
• Create novel and stunning 2D and 3D multivariate data visualizations with R.
• Set up and estimate a Principal Components Analysis (PCA).

Rating: 4.61538

Level: All Levels

Duration: 12 hours

Instructor: Geoffrey Hubona, Ph.D.