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Applied Multivariate Analysis with R

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Applied Multivariate Analysis with R

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

4.0

(406)

4613 enrolled on this course

Last updated 11/2021

Geoffrey Hubona, Ph.D.

₹ 2299.0

₹ 3799.0

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Lessons

Quizzes

Duration

13 Hours

Skill level

Beginner

Language

English

Certificate

Yes

Full lifetime access

Yes

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 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.
Who this course is for:
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.

What you'll 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).

Formulate and estimate a Multidimensional Scaling (MDS) problem.

Group similar (or dissimilar) data with Cluster Analysis techniques.

Estimate and interpret an Exploratory Factor 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.

Requirements

You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
No previous design experience is needed.
No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures

Expand All Sections

1-Introduction to Multivariate Data and Analysis

1.1-Introduction to Multivariate Analysis (MVA) Course

1.2-Materials for Section 1 Introduction to MV Data and Analysis

1.3-What is "Multivariate Analysis" ?

1.4-Missing Values and the Measure Dataset

1.5-Other Multivariate Datasets

1.6-Covariance, Correlation and Distance (part 1)

1.7-Covariance, Correlation and Distance (part 2)

1.8-Covariance, Correlation and Distance (part 3)

1.9-The Multivariate Normal Density Function

1.10-Setting Up Normality Plots

1.11-Drawing Normality Plots

1.12-Covariance, Correlation and Normality Exercises

2-Visualizing Multivariate Data

2.1-Materials and Exercises for Visualizing Multivariate Data Section

2.2-Covariance and Correlation Matrices with Missing Data (part 1)

2.3-Covariance and Correlation Matrices with Missing Data (part 2)

2.4-Univariate and Multivariate QQPlots of Pottery Data

2.5-Converting Covariance to Correlation Matrices

2.6-Plots for Marginal Distributions

2.7-Outlier Identification

2.8-Chi, Bubble, and other Glyph Plots

2.9-Scatterplot Matrix

2.10-Kernel Density Estimators

2.11-3-Dimensional and Trellis (Lattice Package) Graphics

2.12-More Trellis (Lattice Package) Graphics

2.13-Bivariate Boxplot and ChiPlot Visualizations Exercises

3-Principal Components Analysis (PCA)

3.1-Materials for Principal Components Analysis (PCA) Section

3.2-Bivariate Boxplot Visualization Exercise Solution

3.3-ChiPlot Visualization Exercise Solution

3.4-What is a "Principal Components Analysis" (PCA) ?

3.5-PCA Basics with R: Blood Data (part 1)

3.6-PCA Basics with R: Blood Data (part 2)

3.7-PCA with Head Size Data (part 1)

3.8-PCA with Head Size Data (part 2)

3.9-PCA with Heptathlon Data (part 1)

3.10-PCA with Heptathlon Data (part 2)

3.11-PCA with Heptathlon Data (part 3)

3.12-PCA Criminal Convictions Exercise

4-Multidimensional Scaling (MDS)

4.1-Materials for Multidimensional Scaling Section

4.2-PCA Criminal Convictions Exercise Solution

4.3-Introduction to Multidimensional Scaling

4.4-Classical Multidimensional Scaling (part 1)

4.5-Classical Multidimensional Scaling (part 2)

4.6-Classical Multidimensional Scaling: Skulls Data

4.7-Non-Metric Multidimensional Scaling Example: Voting Behavior

4.8-Non-Metric Multidimensional Scaling Example: WW II Leaders

4.9-Multidimensional Scaling Exercise: Water Voles

5-Cluster Analysis

5.1-Materials for Cluster Analysis Section

5.2-MDS Water Voles Exercise Solution

5.3-Introduction to Cluster Analysis

5.4-Hierarchical Clustering Distance Techniques

5.5-Hierarchical Clustering of Measures Data

5.6-Hierarchical Clustering of Fighter Jets

5.7-K-Means Clustering of Crime Data (part 1)

5.8-K-Means Clustering of Crime Data (part 2)

5.9-Clustering of Romano-British Pottery Data

5.10-K-Means Classifying of Exoplanets

5.11-Model-Based Clustering of Exoplanets

5.12-Finite Mixture Model-Based Analysis

5.13-Cluster Analysis Neighborhood and Stripes Plots

5.14-K-Means Cluster Analysis Crime Data Exercise

6-Exploratory Factor Analysis (EFA)

6.1-Materials for Exploratory Factor Analysis (EFA) Section

6.2-K-Means Crime Data Exercise Solution

6.3-Introduction to Exploratory Factor Analysis (EFA)

6.4-The factanal() Function Explained

6.5-EFA Life Data Example

6.6-EFA Drug Use Data Example

6.7-Comparing EFA with Confirmatory Factor Analysis (CFA)

6.8-EFA Exercise

7-Introduction to Structural Equation Modeling (SEM), QGraph, and SIMSEM

7.1-Introduction to the SEM, QGraph and SIMSEM Course Section with Materials

7.2-Exploratory Factor Analysis (EFA) Exercise Solution

7.3-Specify and Estimate Drug Use SEM Model

7.4-Specify and Estimate Alienation SEM Model

7.5-QGraph Visualizations

7.6-SIMSEM Package Simulation Capabilities (part 1)

7.7-SIMSEM Package Simulation Capabilities (part 2)

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