# InfoCoBuild

## 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 nptel.ac.in)

 Introduction

 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 - Multivariate Inferential Statistics Lecture 13 - Multivariate Inferential Statistics (cont.) Lecture 14 - Analysis of Variance (ANOVA) Lecture 15 - Analysis of Variance (ANOVA) (cont.) Lecture 16 - Multivariate Analysis of Variance (MANOVA) Lecture 17 - Multivariate Analysis of Variance (MANOVA) (cont.) Lecture 18 - Tutorial: Analysis of Variance (ANOVA) Lecture 19 - Tutorial: Analysis of Variance (ANOVA) (cont.) Lecture 20 - Multivariate Analysis of Variance (MANOVA): Case Study Lecture 21 - Multiple Regression: Introduction Lecture 22 - Multiple Linear Regression: Sampling Distribution of Regression Coefficients Lecture 23 - Multiple Linear Regression: Model Adequacy Tests Lecture 24 - Multiple Linear Regression: Test of Assumptions Lecture 25 - Multiple Linear Regression: Model Diagnostics Lecture 26 - Multiple Linear Regression: 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 - Principal Component Analysis: Model Adequacy and Interpretation Lecture 32 - Regression Modeling using SPSS Lecture 33 - Factor Analysis Lecture 34 - Factor Analysis - Estimation and Model Adequacy Testing Lecture 35 - Factor Analysis - Model Adequacy, Rotation, Factor Scores and Case Study Lecture 36 - Cluster Analysis Lecture 37 - Cluster Analysis (cont.) Lecture 38 - Introduction to Structural Equation Modeling Lecture 39 - Structural Equation Modeling: Measurement Model Lecture 40 - Structural Equation Modeling: Structural Model Lecture 41 - Correspondence Analysis Lecture 42 - Correspondence Analysis (cont.)

 References 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.