Full course description
Probability and Statistics for Engineers:
Applications-oriented topics with computer-based experience using statistical software for formulating and solving engineering problems.
This deficiency exam will cover the following course objectives:
- Understand the differences between probability and statistics
- Be able to recognize and use common discrete and continuous probability functions
- Use sample statistics to draw inferences about a population of interest through hypothesis testing of means, variances, and proportions
- Build simple and multiple linear regression empirical models from data
- Understand and apply basic statistical process control charts and analyses
Who this deficiency exam designed for:
Students who are either admitted with deficiencies to our Industrial Engineering graduate programs in the School of Computing, Informatics, and Decision Systems Engineering (CIDSE) at Arizona State University or students who are interested in becoming eligible for this program. Students may also complete this deficiency exam if they have general interest in the topic area.
The course syllabus is available here.
What you'll receive:
You will receive a certificate of completion with your grade that you can attach in your graduate application for any graduate program within CIDSE. Students may also complete this deficiency exam if they have general interest in the topic area. Please email your certificate to email@example.com. If you’re a prospective student, you can upload it to your application.
Meet the Deficiency Exam Coordinator
Linda Chattin, Principal Lecturer, CIDSE
Linda Chattin was a lecturer from 1994-1999 in the Industrial Engineering department at the State University of New York and has been teaching at Arizona State since 2000. She teaches undergraduate courses such as: Introduction to Engineering Design, Statistics and Probability for Engineers, Discrete Operations Research, and Economic Analysis for Engineers. She is currently a lecturer in the School of Computing, Informatics, and Decision Systems Engineering. Her areas of expertise are operations research and probabilistic modeling. Chattin received the A. Alan B. Pritsker Outstanding IE Teacher Award in 2009, 2010 and 2013.