ENME392: Statistical Methods for Product and Processes Development

Credits: 3

Description

Prerequisite: Minimum grade of C- in MATH241 and ENME202.
Restriction: Permission of the Mechanical Engineering Department.
Integrated statistical methodology for the improvement of products and processes in terms of performance, quality and cost. Designed experimentation. Statistical process control. Software application. Laboratory activities.Delivered via ITV.

Semesters Offered

Fall 2017, Spring 2018, Summer 2018, Fall 2018, Spring 2019, Summer 2019, Fall 2019, Spring 2020, Fall 2020, Spring 2021, Summer 2021, Fall 2021, Spring 2022, Summer 2022, Fall 2022, Spring 2023, Summer 2023, Fall 2023, Spring 2024, Summer 2024, Fall 2024, Spring 2025, Fall 2025, Spring 2026, Summer 2026, Fall 2026
Testudo | Syllabus

Learning Objectives

This course covers the fundamental aspects of probability and statistics. The overall objective is for students to gain an appreciation of the inherent uncertainty and errors in all engineering and scientific data, and to provide the basic tools from probability and statistics to quantify these uncertainties.

Topics Covered

  • Week 1: Course info; use of statistics in engineering; sample mean and variance. Event, sample space, probability, trees; counting rules, permutations, combinations.
  • Week 2: Venn diagrams; addition and multiplication rules; conditional probability. Random variables, distributions, expected value and variance.
  • Week 3: Functions of random variables; error propagation. Bernoulli, binomial, hypergeometric, and Poisson distributions.
  • Week 4: Normal distributions; exponential distributions.
  • Week 5: Review and problem solving; Exam 1.
  • Week 6: Sampling, estimators, Central Limit Theorem; confidence intervals (concept, prediction interval, mean, proportion).
  • Week 7: Confidence intervals (variance, two means); confidence intervals (paired observations, two proportions, two variances).
  • Week 8: Spring Break.
  • Week 9: Hypothesis testing (concept, p-value, type I and II errors, mean); hypothesis testing (proportion, variance, two means).
  • Week 10: Hypothesis testing (paired observations, two proportions, two variances); hypothesis testing (goodness-of-fit, independence, sign).
  • Week 11: Review and problem solving; Exam 2.
  • Week 12: Linear regression; multiple regression; analysis of variance (ANOVA).
  • Week 13: Analysis of variance (ANOVA).
  • Week 14: Statistical process control (SPC); design of experiments (DOE).
  • Week 15: Design of experiments (DOE); review and problem solving.
  • Week 16: Final Exam.

Learning Outcomes

After successfully completing this course you will be able to:

● apply probability theory fundamentals;
● master discrete and continuous probability distributions;
● apply principles of estimation, construct confidence intervals, perform hypothesis testing, conduct analysis of variance, regression analysis and design of experiments;
● develop skills in summarizing and visualizing data, performing statistical inference, and interpreting the implications of data analysis in engineering.

Additional Course Information

Required Resources 

Course Website: elms.umd.edu

Book: Statistics for Engineers and Scientists, by Navidi, McGraw-Hill, 6th edition

UMD Piazza: https://umd.instructure.com/courses/1399642/external_tools/90732

Virtual Study Agent: You will note the link to the Virtual Study Agent on the ELMS home page. This is an excellent resource to answer questions that you may have about course logistics and content, provide guidance on homework problems, generate additional practice problems and solutions, and even write computer code! Become familiar with and utilize this resource often.