This course provides an introduction to multivariate analysis with a practical focus on problem solving and learning tools such as Excel and R. Sample geometry and random sampling is covered in Part 4. In Part 5, theorems with proofs are introduced for the Multivariate Normal Distribution, followed by Maximum Likelihood Estimation in Part 6, then Problem Solving and Excel in Part 7. Part 8 introduces inferences about a Mean Vector, while Part 9 covers comparisons of several Multivariate Means using Excel. Part 11 dives into Introduction to Linear Regression including simple, multiple and multivariate. Part 12 covers Ordinary Least Squares and Gauss-Markov Theorem with Proof. Fitting Linear Regression Models is reviewed in Part 13, followed by principal components analysis in Part 14.Factor Analysis is introduced in Excel in Part 15, and clustering is covered in Part 17.
Overview
Syllabus
Multivariate Analysis - Introduction/Theory (Part 0).
Multivariate Analysis - Introduction/Problem Solving (Part 1).
Multivariate Analysis - Introduction/Excel (Part 2).
Multivariate Analysis - Introduction/R (Part 3).
Multivariate Analysis - Sample Geometry and Random Sampling (Part 4).
Multivariate Normal Distribution - Theorems with proofs (Part 5).
Multivariate Normal Distribution - Maximum Likelihood Estimation (Part 6).
Multivariate Normal Distribution - Problem Solving (Part 7).
Multivariate Normal Distribution - Excel (Part 8).
Inferences About a Mean Vector - Excel (Part 9).
Comparisons of Several Multivariate Means - Excel (Part 10).
Introduction to Linear Regression - Simple, Multiple, Multivariate (Part 11).
Ordinary Least Squares and Gauss-Markov Theorem with Proof (Part 12).
Fitting Linear Regression Models - Excel (Part 13).
Principal Components Analysis - PCA Introduction (Part 14).
Factor Analysis - A short introduction in Excel (Part 16).
Clustering - Part 17.
Taught by
ProbablyMaxy
