Download course syllabus - Lyle School of Engineering

SMU ENGINEERING
SM
ENGINEERING MANAGEMENT, INFORMATION AND SYSTEMS DEPARTMENT
PROBABILITY AND STATISTICS FOR ENGINEERS
EMIS 7370/5370 /STAT 5340
Fall 2009 Semester
COURSE SYLLABUS
COURSE DESCRIPTION
This course is an introduction to fundamentals of probability, probability distributions and
statistical techniques used by engineers and physical scientists. Topics include basic
concepts and rules of probability, random variables, probability distributions, expectation
and variance, sampling and sampling distributions, statistical analysis techniques, statistical
inference – estimation and tests of hypothesis, correlation and regression, and analysis of
variance.
COURSE OBJECTIVES
To prepare students with diverse technical backgrounds and objectives with fundamental
probabilistic & statistical concepts, methods, and techniques for use in continuing graduate
studies and in engineering & engineering management through a balance of theory and
application involving engineering decision making, including situations in which uncertainty
and risk are important. Emphasis is placed on problem definition, solution and interpretation
of results.
PREREQUISITES
MATH 2339 or equivalent
TEXTBOOK
Probability and Statistics for Engineers and Statistics, Ronald E. Walpole, 8th ed., McMillan,
NY, 2002, ISBN 0-13-041529-4.
INSTRUCTOR
Dr. Jerrell T. Stracener, SAE Fellow
Updated: 8.23.09
COURSE REQUIREMENTS
Homework:
Examinations:
Project:
Selected problems to be graded
Midterm and Final Exam
Required for graduate students only
GRADING POLICY
Homework
Midterm Exam
Final Exam
Project
Graduate
25%
25%
35%
15%
Undergraduate
40%
30%
30%
COURSE SCHEDULED TOPICS
1. Introduction, Overview, Probability - Basic Concepts and Approaches, Probability Counting Techniques
2. Probability - Independence & Fundamental Rules, Conditional Probability and Bayes'
Theorem
3. Discrete Random Variables and Probability Distributions, Binomial, Negative
Binomial and Geometric Distributions
4. Hypergeometric and Poisson Distributions, Continuous Random Variables and
Probability Distributions
5. Normal (Gaussian) & Lognormal Distributions
6. Exponential & Weibull Distributions
7. Gamma & Beta Distributions, t-,Chi-Squared & F-Distributions
8. Functions of Random Variables, Sampling & Sampling Distributions
9. Statistical Analysis - Descriptive Statistics
10. Midterm Exam
11. Statistical Analysis - Graphical Techniques
12. Estimation - Basic Concepts & Estimation of Proportions
13. Estimation of Means, Estimation of Standard Deviation & Percentiles
14. Test of Hypothesis - Basic Concepts & Test of Proportions ,Tests of Means and
Variances , Joint Probability Distributions
15. Covariance & Correlation, Simple Linear Regression
16. Design of Experiments & One Factor Experiments, Randomized Block Experiments
17. Course Recap & Project Presentations
18. Final Exam