Applied Multivariate Statistical Modeling ZDEPWJ38YC

URI	`m2://res/ZDEPWJ38YC`
Kind	course
Creators	Jhareswar Maiti (instructor)
Date	2021-01-03
Scope	in
Ids	course 03w5sq511:111105091

Expressions

e1: edition

Language	en
Date	2021-01-03

m1: page, IIT Kharagpur (record). nptel 111105091
m2: page, 2021-01-03 (record). nptel 111105091

Contents

part 1: week-01
session 1: Lecture 01 : Introduction to Multivariate Statistical Modeling
session 2: Lecture 02 : Introduction to Multivariate Statistical Modeling: Data types, models, and modeling
session 3: Lecture 03 : Statistical approaches to model building
session 4: Lecture 04 : Statistical approaches to model building (Contd)
session 5: Lecture 05 : Univariate Descriptive Statistics
session 6: Lecture 06 : Univariate Descriptive Statistics (Contd)
part 2: week-02
session 7: Lecture 07 : Normal Distribution and Chi-squared Distribution
session 8: Lecture 08 : t-distribution, F-distribution, and Central Limit Theorem
session 9: Lecture 09 : Univariate inferential statistics: Estimation
session 10: Lecture 10 : Univariate Inferential Statistics: Estimation(Contd)
session 11: Lecture 11 : Univariate Inferential Statistics: Hypothesis Testing
session 12: Lecture 12 : Hypothesis Testing(contd): Decision Making Scenarios
part 3: week-03
session 13: Lecture 13 : Multivariate Descriptive Statistics: Mean Vector
session 14: Lecture 14 : Multivariate Descriptive Statistics: Covariance Matrix
session 15: Lecture 15 : Multivariate Descriptive Statistics: Correlation Matrix
session 16: Lecture 16 : Multivariate Descriptive Statistics: Relationship between correlation
part 4: week-04
session 17: Lecture 17 : Multivariate Normal Distribution
session 18: Lecture 18 : Multivariate Normal Distribution(Contd)
session 19: Lecture 19 : Multivariate Normal Distribution (Contd): Geometrical Interpretation
session 20: Lecture 20 : Multivariate Normal Distribution (Contd): Examining data for multivariate normal
part 5: week-05
session 21: Lecture 21 : Multivariate Inferential Statistics: Basics and Hotelling T - square statistic
session 22: Lecture 22 : Multivariate Inferential Statistics: Confidence Region
session 23: Lecture 23:Multivariate Inferential Statistics:Simultaneous confidence interval & Hypothesis testing
session 24: Lecture 24:Multivariate Inferential Statistics: Hypothesis testing for equality of two population
part 6: week-06
session 25: Lecture 25 : Analysis of Variance (ANOVA)
session 26: Lecture 26 : Analysis of Variance (ANOVA): Decomposition of Total sum of squares
session 27: Lecture 27 : Analysis of Variance (ANOVA): Estimation of Parameters and Model Adequacy tests
session 28: Lecture 28 : Two-way and Three-way Analysis of Variance (ANOVA)
session 29: Lecture 29 : Tutorial ANOVA
session 30: Lecture 30 : Tutorial ANOVA (Contd)
part 7: week-07
session 31: Lecture 31: Multivariate Analysis of Variance (MANOVA): Conceptual Model
session 32: Lecture 32: Multivariate Analysis of Variance (MANOVA): (SSCP)
session 33: Lecture 33: Multivariate Analysis of Variance (MANOVA): (SSCP)(contd.)
session 34: Lecture 34: Multivariate Analysis of Variance (MANOVA): Estimation and Hypothesis testing
session 35: Lecture 35: MANOVA Case Study
part 8: week-08
session 36: Lecture 36 : Multiple Linear Regression: Introduction
session 37: Lecture 37 : Multiple Linear Regression: Assumptions and Estimation of model parameters
session 38: Lecture 38 : Multiple Linear Regression: Sampling Distribution of parameter estimates
session 39: Lecture 39 : Multiple Linear Regression: Sampling Distribution of parameter estimates (contd)
session 40: Lecture 40 : Multiple Linear Regression: Model Adequacy Tests
session 41: Lecture 41 : Multiple Linear Regression: Model Adequacy Tests(contd)
session 42: Lecture 42 : Multiple Linear Regression: Test of Assumptions
session 43: Lecture 43 : MLR-Model diagnostics
session 44: Lecture 44 : MLR-case study
part 9: week-09
session 45: Lecture 45: Multivariate Linear Regression: conceptual model and assumptions
session 46: Lecture 46: Multivariate Linear Regression: Estimation of parameters
session 47: Lecture 47: Multivariate Linear Regression: Estimation of parameters (contd.)
session 48: Lecture 48: Multiple Linear Regression: Sampling Distribution of parameter estimates
session 49: Lecture 49: Multivariate Linear Regression: Model Adequacy Tests
session 50: Lecture 50: Multiple Linear Regression: Model Adequacy Tests(contd)
session 51: Lecture 51: Regression modeling using SPSS
part 10: week-10
session 52: Lecture 52 : Principal Component Analysis (PCA): Conceptual Model
session 53: Lecture 53 : Principal Component Analysis (PCA): Extraction of Principal components (PCs)
session 54: Lecture 54 : Principal Component Analysis (PCA): Model Adequacy and Interpretation
session 55: Lecture 55 : Principal Component Analysis (PCA): Model Adequacy and Interpretation (contd)
part 11: week-11
session 56: Lecture 56: Factor Analysis: Basics and Orthogonal factor models
session 57: Lecture 57: Factor Analysis: Types of models and key questions
session 58: Lecture 58: Factor Analysis: Parameter Estimation
session 59: Lecture 59: Factor Analysis: Parameter Estimation(contd)
session 60: Lecture 60: Factor Analysis: Model Adequacy tests and factor rotation
session 61: Lecture 61: Factor Analysis: Factor scores and case study
part 12: week-12
session 62: Lecture 62 : Introduction to Structural Equation Modeling (SEM)
session 63: Lecture 63 : Introduction to Structural Equation Modeling (SEM)(contd)
session 64: Lecture 64 : Structural Equation Modeling (SEM): Confirmatory factor analysis (Measurement Model )
session 65: Lecture 65 : Structural Equation Modeling (SEM): Confirmatory factor analysis (contd)
session 66: Lecture 66 : Structural Equation Modeling (SEM): Structural Model (Path model)
session 67: Lecture 67 : Structural Equation Modeling (SEM): Structural Model(contd)
part 13: Live Session
session 68: Live Session 03-03-2021
session 69: Live Session 14-03-2022

Catalogue records

nptel: 111105091 (manifestation)
nptel_video: 111105091 (manifestation)