Applied Multivariate Analysis with R

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

4.35 (361 reviews)
Udemy
platform
English
language
Data & Analytics
category
4,309
students
12 hours
content
Jul 2020
last update
$59.99
regular price

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).

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.

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.

Content

Introduction to Multivariate Data and Analysis

Introduction to Multivariate Analysis (MVA) Course
Materials for Section 1 Introduction to MV Data and Analysis
What is "Multivariate Analysis" ?
Missing Values and the Measure Dataset
Other Multivariate Datasets
Covariance, Correlation and Distance (part 1)
Covariance, Correlation and Distance (part 2)
Covariance, Correlation and Distance (part 3)
The Multivariate Normal Density Function
Setting Up Normality Plots
Drawing Normality Plots
Covariance, Correlation and Normality Exercises

Visualizing Multivariate Data

Materials and Exercises for Visualizing Multivariate Data Section
Covariance and Correlation Matrices with Missing Data (part 1)
Covariance and Correlation Matrices with Missing Data (part 2)
Univariate and Multivariate QQPlots of Pottery Data
Converting Covariance to Correlation Matrices
Plots for Marginal Distributions
Outlier Identification
Chi, Bubble, and other Glyph Plots
Scatterplot Matrix
Kernel Density Estimators
3-Dimensional and Trellis (Lattice Package) Graphics
More Trellis (Lattice Package) Graphics
Bivariate Boxplot and ChiPlot Visualizations Exercises

Principal Components Analysis (PCA)

Materials for Principal Components Analysis (PCA) Section
Bivariate Boxplot Visualization Exercise Solution
ChiPlot Visualization Exercise Solution
What is a "Principal Components Analysis" (PCA) ?
PCA Basics with R: Blood Data (part 1)
PCA Basics with R: Blood Data (part 2)
PCA with Head Size Data (part 1)
PCA with Head Size Data (part 2)
PCA with Heptathlon Data (part 1)
PCA with Heptathlon Data (part 2)
PCA with Heptathlon Data (part 3)
PCA Criminal Convictions Exercise

Multidimensional Scaling (MDS)

Materials for Multidimensional Scaling Section
PCA Criminal Convictions Exercise Solution
Introduction to Multidimensional Scaling
Classical Multidimensional Scaling (part 1)
Classical Multidimensional Scaling (part 2)
Classical Multidimensional Scaling: Skulls Data
Non-Metric Multidimensional Scaling Example: Voting Behavior
Non-Metric Multidimensional Scaling Example: WW II Leaders
Multidimensional Scaling Exercise: Water Voles

Cluster Analysis

Materials for Cluster Analysis Section
MDS Water Voles Exercise Solution
Introduction to Cluster Analysis
Hierarchical Clustering Distance Techniques
Hierarchical Clustering of Measures Data
Hierarchical Clustering of Fighter Jets
K-Means Clustering of Crime Data (part 1)
K-Means Clustering of Crime Data (part 2)
Clustering of Romano-British Pottery Data
K-Means Classifying of Exoplanets
Model-Based Clustering of Exoplanets
Finite Mixture Model-Based Analysis
Cluster Analysis Neighborhood and Stripes Plots
K-Means Cluster Analysis Crime Data Exercise

Exploratory Factor Analysis (EFA)

Materials for Exploratory Factor Analysis (EFA) Section
K-Means Crime Data Exercise Solution
Introduction to Exploratory Factor Analysis (EFA)
The factanal() Function Explained
EFA Life Data Example
EFA Drug Use Data Example
Comparing EFA with Confirmatory Factor Analysis (CFA)
EFA Exercise

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

Introduction to the SEM, QGraph and SIMSEM Course Section with Materials
Exploratory Factor Analysis (EFA) Exercise Solution
Specify and Estimate Drug Use SEM Model
Specify and Estimate Alienation SEM Model
QGraph Visualizations
SIMSEM Package Simulation Capabilities (part 1)
SIMSEM Package Simulation Capabilities (part 2)

Screenshots

Applied Multivariate Analysis with R - Screenshot_01Applied Multivariate Analysis with R - Screenshot_02Applied Multivariate Analysis with R - Screenshot_03Applied Multivariate Analysis with R - Screenshot_04

Reviews

Max
November 30, 2022
Worst lecture so far! No structure at all, teacher just talks, does not write or execute code so that one is able to learn from it. Even skips code. To theroetical...
Jorge
June 3, 2022
Muy repetitivo, usa los ejemplos de las ayudas del sistema, (eso lo puedo hacer yo sólo) No construye sobre temas nuevos, complementarios. Aunque el temario es completo, y comparte materiales, se salta muchas partes, poca formalidad.
Luca
September 30, 2021
Teacher seemed to does not know what he was talking about, just reading slides and hardly execute or explain the code
Gonçalo
August 26, 2020
Very interesting, well-documented course and easy to follow. The first 6 sections are adequately explained and documented, meaning users will get an adequate idea of the content and usage of such statistical approaches. Unfortunately not the case for section 7, where materials are scarce, do not match the videos, and it did not make the subject clear for me.
Aureliano
May 20, 2020
Bom curso. Bem intuitivo. Mas exige do participante conhecimentos prévios do R e R-Studio. A parte de Equações Estruturais é muito superficial.
Jacob
March 30, 2020
The material is helpful, but the instructor speaks in a way that seems very unplanned and unstructured and makes some of the material sound more confusing than it is. Although he seems to know what he's doing he doesn't explain well.
Aniko
May 15, 2017
The course mixes info for those who are completely new to R and those who have a bit more experience. I expected the latter so for me information such as how to look at the database are needless. I am also watching with X1.5 speed.
Sven
April 11, 2017
Rating is done during course 1 not finished finally - so far covariance and correlation were shown but not deeply explained for what they are used for (but therefore it is explained several times, that a covariance matrix is symmetric which is obvious) Lots of technical repetitions and distractions (how to run a code, etc.) which is not needed to explain because this course is not meant to be a basic course.
Jorge
February 15, 2017
Need more explanations with some of the fuctions, but in general, for newbies like me, is ok. The last two video lectures need a little more explanations. Anyway great so far.
FNU
January 12, 2017
Too dense, everything is thrown in the pot at once, and a lot of times, the professor gets confused. It takes a lot of energy and double checking to grasp the flow of the topic. Still, there are some useful things too, in it.
Pallavi
June 12, 2016
TheinstructorhasdoneagreatjobinexplainingtheconceptsofMVAbut,thecourseaboutRtheonlythinglackinginthesesssionsismanualdemonstrationofRscriptsinsolvingexamplesandquestions
CARLOMARTI149
April 6, 2016
I believe that Geoff Hubona is by far one of the best Udemy class instructors on Statistics , Analysis and Math . His clear insight and explanations using R and Stats concepts really help you understand.
Jose
April 3, 2016
Somewhat disorganized, particularly as the course progresses organization gets worse, but a good introduction to the subject.
Julian
March 3, 2016
I'm studying this subject at uni, so really wanted this course to be good, so that I could get a head start. While there is a whole lot of content here... after going through it most of it, I have to say that I think the "Dr" has actually made me more confused than before.
Garrett
February 4, 2016
Content: 4.5/5 Production Quality: 3/5 Delivery: 2/5 Instructor covers material out of an introductory textbook, typically following examples from the book line-by-line. It's helpful if you're more of a visual person and want to see the examples worked out in real time; he even adds some little tid-bits of his own that may not be included otherwise. Be patient, the instructor often loses track or stumbles over his own presentation. Pretty sure the default video playback (1x) was slowed down around 25% to add extra course time... I recommend using the 1.5x playback speed.

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555462
udemy ID
7/15/2015
course created date
11/22/2019
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