Lecture 1 - Introduction to multivariate statistical modeling
Lecture 2 - Introduction to multivariate statistical modeling (Continued...)
Lecture 3 - Univariate descriptive statistics
Lecture 4 - Sampling distribution
Lecture 5 - Estimation
Lecture 6 - Estimation (Continued...)
Lecture 7 - Hypothesis testing
Lecture 8 - Multivariate descriptive statistics
Lecture 9 - Multivariate descriptive statistics (Continued...)
Lecture 10 - Multivariate normal distribution
Lecture 11 - Multivariate normal distribution (Continued...)
Lecture 12 - Multivariate Inferential Statistics
Lecture 13 - Multivariate Inferential Statistics (Continued...)
Lecture 14 - ANOVA (Analysis of Varianace)
Lecture 15 - Analysis of Variance (Continued...)
Lecture 16 - Multivariate Analysis of Variance (MANOVA)
Lecture 17 - MANOVA (Continued...)
Lecture 18 - Tutorial - ANOVA
Lecture 19 - Tutorial ANOVA (Continued...)
Lecture 20 - MANOVA - Case Study
Lecture 21 - Multiple Regression – Introduction
Lecture 22 - MLR - Sampling distribution of regression coefficients
Lecture 23 - MLR - Model adequacy tests
Lecture 24 - MLR - Test of assumptions
Lecture 25 - MLR - Model diagnostics
Lecture 26 - MLR - Case Study
Lecture 27 - Multivariate Linear Regression
Lecture 28 - Multivariate Linear Regression - Estimation
Lecture 29 - Multivariate Linear Regression - Model Adequacy tests
Lecture 30 - Principal Component Analysis (PCA)
Lecture 31 - PCA - Model Adequacy & Interpretation
Lecture 32 - Regression Modeling using SPSS
Lecture 33 - Factor Analysis
Lecture 34 - Factor Analysis - Estimation & Model Adequacy testing
Lecture 35 - Factor Analysis - Model Adequacy, rotation, factor scores & case study
Lecture 36 - Cluster Analysis
Lecture 37 - Cluster Analysis (Continued...)
Lecture 38 - Introduction to Structural Equation Modeling (SEM)
Lecture 39 - SEM - Measurement Model
Lecture 40 - SEM - Structural Model
Lecture 41 - Correspondence Analysis
Lecture 42 - Correspondence Analysis (Continued...)
