IE605: Engineering Statistics, Aug-Nov 2020

The course provides a strong foundation in theory and methods of modeling randomness and data analysis in engineering applications.

Lecture Hours

Monday 8.30-9.30am
Tuesday 9.30-10.30am
Thursday 10.30am-11.30am


Online via MS Teams

Teaching Assistants

Arun Verma
Fehmina Malik
Debamita Ghosh
Sayan Chatterjee
Harshit Pandey

TA Hours

Timings: Every Monday 2-4pm
Venue : Virtual meetings on request


Review of calculus-based probability concepts, common distributions, expectation, moment generating functions

Sampling statistics, order statistics, properties of sample mean, Central Limit Theorem. Sampling from a Normal distribution

Parameter estimation, maximum likelihood estimators, interval estimates; bias, efficiency and consistency of point estimators

Sampling plans, sequential tests, Hypothesis testing, common tests concerning means, variances

Goodness-of-fit, likelihood ratio test, Neyman-Pearson lemma; Regression models, design of experiments.

Course Grades (to revise)

25 points: Midterm
40 points: 4 Assingments
35 points: Endterm

Reference texts

  • Sheldon Ross, ``Introduction to Probability Models,'' (Eleventh Edition), Elsevier 2014, Download.
  • Douglas C. Montgomery, Larry Faris Thomas and George C. Runger (2003) ``Engineering Statistics, 3rd edition, John Wiley & Sons. Download.
  • Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer (2007) Mathematical Statistics with Applications, 7th edition, Duxbury Resource Center.
  • John A. Rice (1994) Mathematical Statistics and Data Analysis, 3rd edition, Thomson Learning Download.
  • George Casella and Roger Berger (2004) Statistical Inference, 2nd edition, Thomson Learning. Download.
  • Ajit C. Tamhane and Dorothy D. Dunlop (1999) Statistics and Data Analysis: From Elementary to Intermediate, Prentice Hall
  • Tutorials

  • Tutorial 1
  • Tutorial 2
  • Tutorial 3
  • Tutorial 4
  • Tutorial 5