CHEE 209 is an introductory course in probability and statistics and develops skills that are essential to engineers of all disciplines. Students from Geological Engineering, Chemical Engineering, Engineering Chemistry and Mining Engineering take this course. Examples from these disciplines will be used to demonstrate the various techniques. The skills acquired in this course are further developed in a more advanced course, CHEE 418 “Strategies for Process Investigations”.
Analysis of Process Data
Personnel
Instructor
| Xiang Li | 403 | xiang.li@queensu.ca | 613-533-6582 |
TAs
| Kody Kazda | 17kk18@queensu.ca | ||
| Zaid Marfatia | zaid.marfatia@queensu.ca | ||
| Moein Mousavi | 17smm17@queensu.ca | ||
| Behnam Nourmohammadi Khiarak | 20bnk@queensu.ca |
Course Description
Statistical methods for analyzing and interpreting process data are discussed, with special emphasis on techniques for continuous improvement of process operations. Topics include: role of data in assessing process operation, identifying major problems, graphical and numerical summaries, principles of valid inference, probability distributions for discrete and continuous data, process capability, comparing process performance to target values, comparing performances of two processes, control charts, and an introduction to linear regression analysis.
Prerequisites: APSC 171 (Calculus I), APSC 172 (Calculus II), APSC 174 (Introduction to Linear Algebra)
(27/0/0/15/0) (Mathematics/Natural Sciences/Complementary Studies/Engineering Science/Engineering Design)
Objectives and Outcomes
Specific course learning outcomes (CLO) include:
| CLO | DESCRIPTION | INDICATORS |
| CLO1 | Summarize, visualize and interpret data using tabular and graphical methods. | KB-Mathematics KB-ES-ApplMath (b) |
| CLO2 | Apply simple discrete probability models to analyze data related to quality such as particle size and ore concentration, and to evaluate risk factors such as safety and environmental compliance. | KB-ES-ApplMath (b) PA-Formulate |
| CLO3 | Apply continuous probability models to assist in decision making with applications to quality improvement, resource estimation, safety and environmental compliance. | KB Mathematics PA-Evaluate |
| CLO4 | Formulate confidence intervals and hypothesis tests for the sample average and sample variance under standard conditions. | KB Mathematics |
| CLO5 | Develop and analyze linear models to describe and predict process and laboratory behavior. |
KB-ES-ApplMath (b) PA-Formulate |
| CLO6 | Apply computer software to solve statistical problems. | KB Math (b)
PA (c) |
This course develops the following attributes at the 2nd year level:
Knowledge Base (KB): Mathematics Demonstrate competence in university-level mathematics.
Knowledge base, Engineering Science (KB-ES): Applied Math (b) Apply numerical and statistical methods to analyze, interpret and model data.
Problem Analysis (PA): Formulate Develop appropriate frameworks for solving complex engineering problems. Evaluate Analyze solutions to complex engineering problems to draw conclusions.
Engineering Tools (ET): Apply Apply and manage appropriate techniques, apparatus, databases, models, tools, and/or processes to accomplish a task.
Relevance to the Program
Course Structure and Activities
This course represents a study period of 6 weeks. Learners can expect to invest on average 16-18 hours per week in this course. Learners who adhere to a pre-determined study schedule are more likely to successfully complete the course.
Resources
Optional Textbook: Miller & Freund's Probability and Statistics for Engineers, 8th or 9th edition, Richard A. Johnson (author)
Required Hardware/Software: Students must have a reliable internet connection and hardware that are compatible with online learning and remote proctoring system requirements.
Course notes and other course-related material (SUGGESTED CONTENT) and all other course material is accessible via OnQ.