Applied Multivariate Analysis with R

Course Curriculum

Chapter 1: Introduction to Multivariate Data and Analysis

Lecture 1: Introduction to Multivariate Analysis (MVA) Course

Lecture 2: Materials for Section 1 Introduction to MV Data and Analysis

Lecture 3: What is "Multivariate Analysis" ?

Lecture 4: Missing Values and the Measure Dataset

Lecture 5: Other Multivariate Datasets

Lecture 6: Covariance, Correlation and Distance (part 1)

Lecture 7: Covariance, Correlation and Distance (part 2)

Lecture 8: Covariance, Correlation and Distance (part 3)

Lecture 9: The Multivariate Normal Density Function

Lecture 10: Setting Up Normality Plots

Lecture 11: Drawing Normality Plots

Lecture 12: Covariance, Correlation and Normality Exercises

Chapter 2: Visualizing Multivariate Data

Lecture 1: Materials and Exercises for Visualizing Multivariate Data Section

Lecture 2: Covariance and Correlation Matrices with Missing Data (part 1)

Lecture 3: Covariance and Correlation Matrices with Missing Data (part 2)

Lecture 4: Univariate and Multivariate QQPlots of Pottery Data

Lecture 5: Converting Covariance to Correlation Matrices

Lecture 6: Plots for Marginal Distributions

Lecture 7: Outlier Identification

Lecture 8: Chi, Bubble, and other Glyph Plots

Lecture 9: Scatterplot Matrix

Lecture 10: Kernel Density Estimators

Lecture 11: 3-Dimensional and Trellis (Lattice Package) Graphics

Lecture 12: More Trellis (Lattice Package) Graphics

Lecture 13: Bivariate Boxplot and ChiPlot Visualizations Exercises

Chapter 3: Principal Components Analysis (PCA)

Lecture 1: Materials for Principal Components Analysis (PCA) Section

Lecture 2: Bivariate Boxplot Visualization Exercise Solution

Lecture 3: ChiPlot Visualization Exercise Solution

Lecture 4: What is a "Principal Components Analysis" (PCA) ?

Lecture 5: PCA Basics with R: Blood Data (part 1)

Lecture 6: PCA Basics with R: Blood Data (part 2)

Lecture 7: PCA with Head Size Data (part 1)

Lecture 8: PCA with Head Size Data (part 2)

Lecture 9: PCA with Heptathlon Data (part 1)

Lecture 10: PCA with Heptathlon Data (part 2)

Lecture 11: PCA with Heptathlon Data (part 3)

Lecture 12: PCA Criminal Convictions Exercise

Chapter 4: Multidimensional Scaling (MDS)

Lecture 1: Materials for Multidimensional Scaling Section

Lecture 2: PCA Criminal Convictions Exercise Solution

Lecture 3: Introduction to Multidimensional Scaling

Lecture 4: Classical Multidimensional Scaling (part 1)

Lecture 5: Classical Multidimensional Scaling (part 2)

Lecture 6: Classical Multidimensional Scaling: Skulls Data

Lecture 7: Non-Metric Multidimensional Scaling Example: Voting Behavior

Lecture 8: Non-Metric Multidimensional Scaling Example: WW II Leaders

Lecture 9: Multidimensional Scaling Exercise: Water Voles

Chapter 5: Cluster Analysis

Lecture 1: Materials for Cluster Analysis Section

Lecture 2: MDS Water Voles Exercise Solution

Lecture 3: Introduction to Cluster Analysis

Lecture 4: Hierarchical Clustering Distance Techniques

Lecture 5: Hierarchical Clustering of Measures Data

Lecture 6: Hierarchical Clustering of Fighter Jets

Lecture 7: K-Means Clustering of Crime Data (part 1)

Lecture 8: K-Means Clustering of Crime Data (part 2)

Lecture 9: Clustering of Romano-British Pottery Data

Lecture 10: K-Means Classifying of Exoplanets

Lecture 11: Model-Based Clustering of Exoplanets

Lecture 12: Finite Mixture Model-Based Analysis

Lecture 13: Cluster Analysis Neighborhood and Stripes Plots

Lecture 14: K-Means Cluster Analysis Crime Data Exercise

Chapter 6: Exploratory Factor Analysis (EFA)

Lecture 1: Materials for Exploratory Factor Analysis (EFA) Section

Lecture 2: K-Means Crime Data Exercise Solution

Lecture 3: Introduction to Exploratory Factor Analysis (EFA)

Lecture 4: The factanal() Function Explained

Lecture 5: EFA Life Data Example

Lecture 6: EFA Drug Use Data Example

Lecture 7: Comparing EFA with Confirmatory Factor Analysis (CFA)

Lecture 8: EFA Exercise

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

Lecture 1: Introduction to the SEM, QGraph and SIMSEM Course Section with Materials

Lecture 2: Exploratory Factor Analysis (EFA) Exercise Solution

Lecture 3: Specify and Estimate Drug Use SEM Model

Lecture 4: Specify and Estimate Alienation SEM Model

Lecture 5: QGraph Visualizations

Lecture 6: SIMSEM Package Simulation Capabilities (part 1)

Lecture 7: SIMSEM Package Simulation Capabilities (part 2)