Course

Module 1

Module 1: Able to understand the utility of the R system inmore detail (7.5 hours)

1. Introduction to the course & pedagogy
2. Introduction to R
3. Basics of R

Module 2

Module 2: Utility of Python language and multivariate data analytics (7.5 hours)

1. Basics of Python for Data Science.
2. Overview of Multivariate methods.
3. Examining your data.
4. Exploratory factor Analysis.

Module 3

Module 3: Basic of data and relationship between data and variables (7.5 hours)

1. Principal component Analysis.
2. Cluster Analysis.
3. Multiple regressions Analysis.
4. Logistic regression.

Module 4

Module 4: Dimension reduction liked CA and MDS and evaluation of models scientifically (7.5 hours)

1. Other methods for dimension reduction.
2. Model Evaluation techniques.

Course Description

The course is intended to serve as a guide to the principles, assumptions, strengths, limitations, and application of multivariate methods for business analysts and students. This course introduces concepts essential to understanding the rationale of multivariate data analysis. It reviews basic statistical principles and techniques that form the foundation for learning about multivariate methods. It covers most of the core techniques currently used in multivariate analysis. Data evaluation, identification of suitablemodels, estimation methods, and evaluation of the models are explained with examples. Many examples of the application of multivariate methods to the problems in various business domains, including marketing, retail sales, human resource management, operations and supply chain management, finance, and general management.

Course Outcomes& Learning levels

This course aims to provide a comprehensive background in multivariate data analysis. At the end of this course, the students

1. Will be able to explore and pre-process multivariate data in an informed, disciplined way.
2. Will be able to avoid common mistakes in the use of multivariate methods and therebymove toward more sound, correct practices in all phases of the analysis.
3. Will be able to use multivariate models intelligently and get as much out of its application aspossible
4. Will be able to demonstrate the application of multivariate techniques using R and Python inan effective manner.

Evaluation Pattern

* Based on assignments / Tests / Quizzes / Case Studies / Projects / Term paper / Field visit report.

1. Wolfgang Karl Härdle and Léopold Simar (2015), “Applied Multivariate Statistical Analysis”,Fourth Edition, Springer, ISBN 978-3-662-45170-0.
2. Everitt Brian and Hothorn Torsten (2011), “An Introduction to Applied Multivariate Analysis with R”, Springer, ISBN- 1441996508.
3. Barbara G. Tabachnick and Linda S. Fidell (2013) “Using Multivariate Statistics”, Sixth Edition, Pearson, ISBN-10: 0-205-89081-4.
4. Denis, Daniel J. (2020) “Univariate, Bivariate, and Multivariate Statistics Using R QuantitativeTools for Data Analysis and Data Science “, John Wiley & Sons, ISBN: 9781119549932.
5. Denis, Daniel J. (2021) “Applied Univariate, Bivariate, and Multivariate Statistics Using Python: Beginner’s Guide to Advanced Data Analysis, John Wiley & Sons, ISBN 9781119578147.
6. Schumacker, Randall E. (2015) “Using R with Multivariate Statistics”, SAGE Publications, ISBN 9781483377964.