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Course Co-ordinated by IIT Kharagpur
Coordinators
 
Dr J Maiti
IIT Kharagpur

 

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Data driven decision making is the state of the art today. It spreads across all sectors of human civilization. Engineers today gather huge data and seek meaningful knowledge out of these for interpreting the process behavior. Scientists do experiments under controlled environment and analyze them to confirm or reject hypotheses. Managers and administrators use the results out of data analysis for day to day decision making. Data collection and storage is an easy task today. Data-driven decision making now is the way of life. The aim of this course is therefore to build confidence in the students in analyzing 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) Analyzing 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.

Module No.

Module description

Topic

Duration

 

1

 

Background

 

Introduction to multivariate statistical modeling

 

2 hr

 

2

 

Basic univariate statistics

Univariate descriptive statistics

1 hr

Sampling distribution

1 hr

Estimation

2 hr

Hypothesis testing

1 hr

 

3

 

Basic multivariate statistics

Multivariate descriptive statistics

2 hr

Multivariate normal distribution

2 hr

Multivariate Inferential statistics

2 hr

 

 

 

 

 

4

 

 

 

 

 

Multivariate models

Analysis of variance (ANOVA)

2 hr

Multivariate analysis of variance (MANOVA)

2 hr

Tutorial: ANOVA

2 hr

Case study: MANOVA

1 hr

Multiple linear regression (MLR): Introduction

1 hr

MLR: Sampling distribution of regression coefficients

1 hr

MLR: Model adequacy tests

1 hr

MLR: Test of assumptions

1 hr

MLR: Model diagnostics

1 hr

MLR: Case study

1 hr

Multivariate linear regression (MvLR): Introduction

1 hr

MvLR: Estimation

1 hr

MvLR: Model adequacy tests

1 hr

Regression modeling using SPSS

1 hr

Principle component analysis (PCA): Introduction

1 hr

PCA: Model adequacy and interpretation

1 hr

Factor analysis (FA): Introduction

1 hr

FA: Estimation and model adequacy testing

1 hr

FA: Rotation, factor scores, and case study

1 hr

Cluster analysis (CA)

2 hr

Introduction to structural equation modeling (SEM)

1 hr

Correspondence analysis

2 hr


Basic 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.

  1. www.duxbury.com
  2. www.prenhall.com/statistics

Analyzing multivariate data by James Latin, J Dooglas Carrol and Paul E Green, Cengage Learning India Pvt. Ltd., 2003.



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