enme392:

Credits:

Semesters Offered

| 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.