Feb 01, 2023
 HELP 2021-2022 Undergraduate Academic Catalog [ARCHIVED CATALOG] Print-Friendly Page (opens a new window)

# IE 2030 - Applications of Statistics in Industrial Engineering

3 lecture hours 2 lab hours 4 credits
Course Description
This course emphasizes the importance and relevance of probability and statistics, as well as research methods in the field of Industrial Engineering.  The purpose of the course is to further student understanding of the applications of probability and statistics in engineering.  The course will concentrate on data collection, data mining, as well as analysis and inference using statistical methods.  The course is also aimed at broadening statistical skills by having students use a state-of-the-art statistics package (e.g. Minitab, R, etc.) so that meaningful problems can be addressed. (prereq: MA 262  or equivalent or consent of instructor)
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
• Describe and define basic statistical terminology
• Create histograms and identify probability distributions
• Identify and evaluate the clarity of a hypothesis statement
• Identify the specific research question under investigation through clear hypothesis formation
• Perform statistical analyses including working with probability distributions
• Draw inferences from data obtained by testing components and systems, using regression analysis as well as other applicable statistical tests
• Improve communication skills, both written and verbal
• Understand inverse cumulative distribution functions and their role in random number generation

Prerequisites by Topic
• Good understanding of probability, statistical distributions, hypothesis testing, and analysis of variance

Course Topics
• Minitab, R, or other statistics software
• Methods of inquiry
• Probability distributions
• Measurement error and propagation
• Confidence intervals
• Descriptive and inferential statistics
• Hypothesis testing
• Correlation and linear regression
• Multiple regression
• Experimental design
• Statistical report from mined data

Laboratory Topics
• A weekly two-hour lab will use defined projects to exercise student skills as defined in the Course Outcomes section

Coordinator
Dr. Douglas Grabenstetter