Syllabus  |   Lectures  |   Downloads  |   FAQ  |   Ask a question  |  
Course Co-ordinated by IIT Kharagpur
Coordinators
 
Dr J Maiti
IIT Kharagpur

 

Download Syllabus in PDF format



Untitled Document
 

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. The course will help the students by:

(i) Providing guidelines to identify and describe real life problems so that relevant data can be collected, 
(ii) Linking data generation process with statistical distributions, especially in the multivariate domain, 
(iii) Linking the relationship among the variables (of a process or system) with multivariate statistical models, 
(iv) Providing step by step procedure for estimating parameters of a model developed, 
(v) Analysing errors along with computing overall fit of the models, 
(vi) Interpreting model results in real life problem solving, and 
(vii) Providing procedures for model validation.

WeekNo.

Topics

1.

Introduction to Multivariate statistical modelling, Univariate descriptive statistics

2.

Sampling Distribution, Estimation

3.

Hypothesis Testing, Assignment-I

4.

Multivariate descriptive statistics

5.

Multivariate normal distribution

6.

Multivariate Inferential statistics, Assignment-II

7.

Analysis of variance (ANOVA) and Multivariate analysis of variance (MANOVA)

8.

Multiple Linear Regression (MLR)

9.

Multivariate Linear Regression (MvLR), Assignment-III

10.

Principal Component Analysis (PCA), Factor Analysis (FA)

11.

Cluster analysis (CA), Correspondence Analysis (CoA), Assignment-IV

12.

Introduction to structural equation modelling (SEM), SEM – Measurement model.
SEM – Structural model, Assignment-V

Basic Knowledge of Probability and Statistics


  1. Applied multivariate statistical analysis by R A Johnson and D W Wichern, Sixth Edition, PHI, 2012.
  2. Multivariate data analysis by Joseph F. Hair Jr, Rolph E. Anderson, Ronald L Tatham, and William C. Black, Fifth Edition, Pearson Education, 1998.
  3. Principal component analysis by I T Jolliffe, Second Edition, Springer, 2002.
  4. Analysing multivariate data by J Lattin, J D Carroll and P E Green, Cengage Learning, 2010.
  5. Applied multivariate analysis by N H Timm, Springer, 2002.


Important: Please enable javascript in your browser and download Adobe Flash player to view this site
Site Maintained by Web Studio, IIT Madras. Contact Webmaster: nptel@iitm.ac.in