Apr 24, 2024  
2017-2018 Undergraduate Academic Catalog 
    
Catalog Navigation
  Catalog Home
  Academic Calendar
  University Overview
  Enrollment Management Department
  Academic Regulations and Policies
  Student Financial Services
  Academic Departments, Undergraduate Degree Programs, Minors and Certificates
  Degree Programs, Minors and Certificates
  Reserve Officer Training Corps (ROTC)
  Course Descriptions
  Administration, Board of Regents, Faculty and Advisory Committees
  Campus Map
  Student Resources
  My Portfolio
HELP
2017-2018 Undergraduate Academic Catalog [ARCHIVED CATALOG]

Facebook this Page (opens a new window)
Tweet this Page (opens a new window)
Add to Portfolio (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, 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, etc.) so that meaningful problems can be addressed. (prereq: MA 262 )
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 or other statistics software
  • Probablity
  • Distributions
  • Measurement error and propagation
  • Confidence intervals
  • Descriptive and inferential statistics
  • Univariate analysis
  • Point and interval estimation
  • Hypothesis testing
  • Bivariate analysis
Laboratory Topics
  • A weekly two-hour lab will use defined projects to exercise student skills as defined in the Course Outcome section
Coordinator
Doug Grabenstetter


Facebook this Page (opens a new window)
Tweet this Page (opens a new window)
Add to Portfolio (opens a new window)