Applied Multivariate Analysis with R
Applied Multivariate Analysis with R. Learn to use R software to conduct PCAs, MDSs, cluster analyses, EFAs and to estimate SEM models.
The name of this course is Applied Multivariate Analysis with R. The knowledge you will get with this indescribable online course is astonishing. Learn to use R software to conduct PCAs, MDSs, cluster analyses, EFAs and to estimate SEM models..
Not only will you be able to deeply internalize the concepts, but also their application in different fields won’t ever be a problem. The instructor is Geoffrey Hubona, Ph.D., one of the very best experts in this field.
Description of this course: Applied Multivariate Analysis with R
Course Description 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 vivo 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 Creador 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.
Requirements of this course: Applied Multivariate Analysis with R
What are the requirements? No specific knowledge or skills are required. Students will need to install the popular no-cost R Console and RStudio software (instructions provided). However, it is helpful if students have some interest and aptitude in quantitative or statistical analysis.
What will you learn in this course: Applied Multivariate Analysis with R?
What am I going to get from this course? Conceptualize and apply multivariate skills and “hands-on” techniques using R software in analyzing vivo data. Create novel and stunning 2D and 3D multivariate data visualizations with R. Set up and estimate a Principal Components Analysis (PCA). Formulate and estimate a Multidimensional Scaling (MDS) problem. Group similar (or dissimilar) data with Cluster Analysis techniques. Estimate and interpret an Exploratory Creador Analysis (EFA). Specify and estimate a Structural Equation Model (SEM) using RAM notation in R. Be knowledgeable about SEM simulation capabilities from the R SIMSEM package.
Target audience of this course: Applied Multivariate Analysis with R
Who is the target audience? Anyone interested in using multivariate analysis technques as a basis for data mining, statistical modeling, and structural equation modeling (SEM) estimation. Practicing quantitative analysis professionals including college and university faculty seeking to learn new multivariate data analysis skills. Undergraduate students looking for jobs in predictive or business analytics fields. Graduate students wishing to learn more applied data analysis techniques and approaches.
Geoffrey Hubona, Ph.D.
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