Applied Multivariate Statistical Modeling

Applied Multivariate Statistical Modeling. Instructor: Dr. J. Maiti, Department of Industrial Engineering and Management, IIT Kharagpur. Data driven decision making is the state of the art today. Engineers today gather huge data and seek meaningful knowledge out of these for interpreting the process behaviour. Scientists do experiments under controlled environment and analyse them to confirm or reject hypotheses. Managers and administrators use the results out of data analysis for day to day decision making. As the data collected and stored are multidimensional, to extract knowledge out of it requires statistical analysis in the multivariate domain. The aim of this course is therefore to build confidence in the students in analysing and interpreting multivariate data. (from

Lecture 22 - Principal Component Analysis: Model Adequacy and Interpretation

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Lecture 01 - Introduction to Multivariate Statistical Modeling, Part I
Lecture 02 - Introduction to Multivariate Statistical Modeling, Part II
Lecture 03 - Univariate Descriptive Statistics
Lecture 04 - Sampling Distribution
Lecture 05 - Estimation Part I
Lecture 06 - Estimation Part II
Lecture 07 - Hypothesis Testing
Lecture 08 - Multivariate Descriptive Statistics
Lecture 09 - Multivariate Descriptive Statistics (cont.)
Lecture 10 - Multivariate Normal Distribution
Lecture 11 - Multivariate Normal Distribution (cont.)
Lecture 12 - Analysis of Variance (ANOVA)
Lecture 13 - Analysis of Variance (ANOVA) (cont.)
Lecture 14 - Multivariate Analysis of Variance (MANOVA)
Lecture 15 - Multivariate Analysis of Variance (MANOVA) (cont.)
Lecture 16 - Multiple Regression: Introduction
Lecture 17 - Multiple Linear Regression: Sampling Distribution of Regression Coefficients
Lecture 18 - Multiple Linear Regression: Model Adequacy Tests
Lecture 19 - Multiple Linear Regression: Test of Assumptions
Lecture 20 - Multiple Linear Regression: Model Diagnostics
Lecture 21 - Principal Component Analysis (PCA)
Lecture 22 - Principal Component Analysis: Model Adequacy and Interpretation
Lecture 23 - Factor Analysis
Lecture 24 - Factor Analysis - Estimation and Model Adequacy Testing
Lecture 25 - Factor Analysis - Model Adequacy, Rotation, Factor Scores and Case Study
Lecture 26 - Introduction to Structural Equation Modeling
Lecture 27 - Structural Equation Modeling - Measurement Model
Lecture 28 - Structural Equation Modeling - Structural Model