Course Name: Applied Multivariate Statistical Modeling

Course abstract

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.


Course Instructor

Media Object

Prof. J. Maiti

Prof. Jhareswar Maiti PhD, Professor, Department of Industrial & Systems Engineering, Indian Institute of Technology (IIT), Kharagpur, India has more than fifteen years of teaching, research and consulting experience on statistical modeling of production and service systems in the areas of Quality Management, Safety Management, Work System Design, and Asset Management. His current research and teaching interests encompass applied data analytics including statistical modeling, machine learning and data mining applications for prediction and decision making. He has published more than 70 papers in international and national journals of repute and more than 30 papers in conference proceedings. Till date, he has supervised 10 PhD candidates to successful completion and currently supervising 8 PhD research candidates. He has been executing a number of Industry-sponsored consulting and Government-funded research projects. He has organized 15+ training programmes and short-term courses for industry participants. A 42 lecture series on "Applied Multivariate Statistical Modeling" of Prof Maiti is available through youtube uploaded by NPTEL (national programme on technology enhanced learning). The current profile of Prof Maiti can be found at IIT Kharagpur website.

Teaching Assistant(s)

SOBHAN SARKAR

Research Scholar at Industrial & Systems Engineering Dept.,
IIT Kharagpur

Supriya Kumar Ghatak

Research Student
Industrial & Systems Engineering Department
IIT Kharagpur

 Course Duration : Jan-Apr 2016

  View Course

 Enrollment : 05-Dec-2015 to 04-Feb-2016

 Exam Date : 24-Apr-2016

Enrolled

1167

Registered

46

Certificate Eligible

37

Certified Category Count

Gold

3

Silver

0

Elite

17

Successfully completed

14

Participation

3

Success

Elite

Gold





Legend

>=90 - Elite+Gold
60-89 - Elite
35-59 - Successfully Completed
<=34 - Certificate of Participation

Final Score Calculation Logic

  • Assignment Score = Average of best 8 out of 12 assignments.
  • Final Score(Score on Certificate)= 60% of Exam Score + 40% of Assignment Score.
Applied Multivariate Statistical Modeling - Toppers list

SAHIL BHATIA 95%

DCB INFRASTRUCTURE

KRISHANU MUKHERJEE 90%

PRAXIS BUSINESS SCHOOL

BUNTY VIRWANI 90%

ABSB CLASSES

KARUNA ARORA 88%

AMAR UJALA PUBLICATIONS LTD

SIRSENDU GHOSH 86%

ALL INDIA INSTITUTE OF MEDICAL SCIENCE

N VENKATESH KUMAR 86%

ACHARYA INSTITUTE OF TECHNOLOGY

NIKHIL SINGH 80%

JSS MEDICAL RESEARCH

SUNEEL DONDAPATI 80%

RVR & JC COLLEGE OF ENGINEERING

RIDDHIMOY GHOSH 79%

ASUTOSH COLLEGE

AMEY GIRISH BHOLE 77%

NA

Assignment

Exam score

Final score

Score Distribution Graph - Legend

Assignment Score: Distribution of average scores garnered by students per assignment.
Exam Score : Distribution of the final exam score of students.
Final Score : Distribution of the combined score of assignments and final exam, based on the score logic.