Statistics for Engineers

Lectures: Tue, Thus 11:30 - 1:00
Instructors: Shashi Prabh, Sunil Kale
Prerequisites: CSD 102 Data Science (or equivalent)

Introduction and course objectives

Statistics has applications in several aspects of engineering, such as, data interpretation, uncertainty analysis in experiments, quality control, machine learning, and simulations of networks amongst others. Topics include fundamentals of linear and introduction to multivariable regression, hypothesis testing, analysis of variance, design of experiments, and simulation. Applications will be drawn from engineering experiments, quality control, sampling, numerical integration, and traffic analysis in data networks. The exercises will require use of Python or any other programming language.

Learning outcomes
The students should be able to:

• Apply the concept of variability in engineering problems
• Apply statistical thinking to various engineering applications
• Analyse given engineering context using statistical software
• Make decisions on quality control, selection of materials, failure, residual life
• Identify the use of appropriate statistical techniques in engineering situations.

Course content, Textbook, Schedule, Grading etc.
See the course description on Auris

Problem Sets

• Problem Set 1

Supporting material for the prerequisites