Lecture 1 - Introduction to Multivariate Statistical Modeling
Lecture 2 - Introduction to Multivariate Statistical Modeling: Data types, models, and modeling
Lecture 3 - Statistical approaches to model building
Lecture 4 - Statistical approaches to model building (Continued...)
Lecture 5 - Univariate Descriptive Statistics
Lecture 6 - Univariate Descriptive Statistics (Continued...)
Lecture 7 - Normal Distribution and Chi-squared Distribution
Lecture 8 - t-distribution, F-distribution, and Central Limit Theorem
Lecture 9 - Univariate Inferential Statistics: Estimation
Lecture 10 - Univariate Inferential Statistics: Estimation (Continued...)
Lecture 11 - Univariate Inferential Statistics: Hypothesis Testing
Lecture 12 - Hypothesis Testing (Continued...): Decision Making Scenarios
Lecture 13 - Multivariate Descriptive Statistics: Mean Vector
Lecture 14 - Multivariate Descriptive Statistics: Covariance Matrix
Lecture 15 - Multivariate Descriptive Statistics: Correlation Matrix
Lecture 16 - Multivariate Descriptive Statistics: Relationship between correlation and covariance matrices
Lecture 17 - Multivariate Normal Distribution
Lecture 18 - Multivariate Normal Distribution (Continued...)
Lecture 19 - Multivariate Normal Distribution (Continued...): Geometrical Interpretation
Lecture 20 - Multivariate Normal Distribution (Continued...): Examining data for multivariate normal distribution
Lecture 21 - Multivariate Inferential Statistics: Basics and Hotelling T-square statistic
Lecture 22 - Multivariate Inferential Statistics: Confidence Region
Lecture 23 - Multivariate Inferential Statistics: Simultaneous confidence interval and Hypothesis testing
Lecture 24 - Multivariate Inferential Statistics: Hypothesis testing for equality of two population mean vectors
Lecture 25 - Analysis of Variance (ANOVA)
Lecture 26 - Analysis of Variance (ANOVA): Decomposition of Total sum of squares
Lecture 27 - Analysis of Variance (ANOVA): Estimation of Parameters and Model Adequacy tests
Lecture 28 - Two-way and Three-way Analysis of Variance (ANOVA)
Lecture 29 - Tutorial ANOVA
Lecture 30 - Tutorial ANOVA (Continued...)
Lecture 31 - Multivariate Analysis of Variance (MANOVA): Conceptual Model
Lecture 32 - Multivariate Analysis of Variance (MANOVA): Assumptions and Decomposition of total sum square and cross products (SSCP)
Lecture 33 - Multivariate Analysis of Variance (MANOVA): Decomposition of total sum square and cross products (SSCP) (Continued...)
Lecture 34 - Multivariate Analysis of Variance (MANOVA): Estimation and Hypothesis testing
Lecture 35 - MANOVA Case Study
Lecture 36 - Multiple Linear Regression: Introduction
Lecture 37 - Multiple Linear Regression: Assumptions and Estimation of model parameters
Lecture 38 - Multiple Linear Regression: Sampling Distribution of parameter estimates
Lecture 39 - Multiple Linear Regression: Sampling Distribution of parameter estimates (Continued...)
Lecture 40 - Multiple Linear Regression: Model Adequacy Tests
Lecture 41 - Multiple Linear Regression: Model Adequacy Tests (Continued...)
Lecture 42 - Multiple Linear Regression: Test of Assumptions
Lecture 43 - MLR-Model diagnostics
Lecture 44 - MLR-case study
Lecture 45 - Multivariate Linear Regression: Conceptual model and assumptions
Lecture 46 - Multivariate Linear Regression: Estimation of parameters
Lecture 47 - Multivariate Linear Regression: Estimation of parameters (Continued...)
Lecture 48 - Multiple Linear Regression: Sampling Distribution of parameter estimates
Lecture 49 - Multivariate Linear Regression: Model Adequacy Tests
Lecture 50 - Multiple Linear Regression: Model Adequacy Tests (Continued...)
Lecture 51 - Regression modeling using SPSS
Lecture 52 - Principal Component Analysis (PCA): Conceptual Model
Lecture 53 - Principal Component Analysis (PCA): Extraction of Principal components (PCs)
Lecture 54 - Principal Component Analysis (PCA): Model Adequacy and Interpretation
Lecture 55 - Principal Component Analysis (PCA): Model Adequacy and Interpretation (Continued...)
Lecture 56 - Factor Analysis: Basics and Orthogonal factor models
Lecture 57 - Factor Analysis: Types of models and key questions
Lecture 58 - Factor Analysis: Parameter Estimation
Lecture 59 - Factor Analysis: Parameter Estimation (Continued...)
Lecture 60 - Factor Analysis: Model Adequacy tests and factor rotation
Lecture 61 - Factor Analysis: Factor scores and case study
