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1
Multivariate Analysis - Introduction/Theory (Part 0)
2
Multivariate Analysis - Introduction/Problem Solving (Part 1)
3
Multivariate Analysis - Introduction/Excel (Part 2)
4
Multivariate Analysis - Introduction/R (Part 3)
5
Multivariate Analysis - Sample Geometry and Random Sampling (Part 4)
6
Multivariate Normal Distribution - Theorems with proofs (Part 5)
7
Multivariate Normal Distribution - Maximum Likelihood Estimation (Part 6)
8
Multivariate Normal Distribution - Problem Solving (Part 7)
9
Multivariate Normal Distribution - Excel (Part 8)
10
Inferences About a Mean Vector - Excel (Part 9)
11
Comparisons of Several Multivariate Means - Excel (Part 10)
12
Introduction to Linear Regression - Simple, Multiple, Multivariate (Part 11)
13
Ordinary Least Squares and Gauss-Markov Theorem with Proof (Part 12)
14
Fitting Linear Regression Models - Excel (Part 13)
15
Principal Components Analysis - PCA Introduction (Part 14)
16
Factor Analysis - A short introduction in Excel (Part 16)
17
Clustering - Part 17
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Develop a comprehensive understanding of multivariate statistical analysis techniques through a 13-hour course that combines theoretical explanations, problem-solving, and practical implementation in R and Excel. Explore key topics including the multivariate normal distribution, hypothesis testing, linear regression models, principal component analysis, factor analysis, and clustering. Gain insights into data reduction, simplification, grouping, and prediction methods applicable to various fields such as machine learning, data science, mathematics, statistics, medicine, economics, and social sciences. Engage with video solutions, theorems, proofs, and hands-on exercises to master concepts from sample geometry and random sampling to discrimination and classification. Build a strong foundation for comprehending advanced multivariate techniques, with a prerequisite knowledge of linear algebra, probability, and statistics. Utilize suggested literature from renowned authors to supplement your learning experience. Read more

Multivariate Statistical Analysis

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