SIE 305 Introduction to Engineering Probability and Statistics

SIE 305 - Introduction to Engineering Probability and Statistics (3) Axioms of probability; discrete and continuous distributions; sampling distributions; engineering applications of statistical estimation; hypothesis testing; confidence intervals.

J. S. Milton and J. C. Arnold, Introduction to Probability and Statistics, 3rd Edition, McGraw-Hill, Inc., 1995.

Also lecture notes by the instructor

Mosteller, F., Fifty Challenging Problems in Probability, with Solutions. Dover Publications, 1987.

Ross, S. M., Introduction to Probability and Statistics for Engineers and Scientists, John Wiley Publishing, 1987.

Papoulis, A., Probability & Statistics. Prentice Hall, 1990.

Walpole, R.E., and Myers, R. H., Probability and Statistics for Engineers and Scientists, 5th Edition. Macmillan.

Gonick, L., The Cartoon Guide to Statistics, Harper Perennial, 1993.

Emmanuel Fernandez, Associate Professor of Systems and Industrial Engineering

1. Differentiation of elementary functions, elementary integration, Taylor series
2. Math 125B, or equivalent

Remedial instruction offered to students as needed. Small review in the classroom. No major problems encountered.

The objective of SIE 305 is to introduce students to the concepts of probability and statistics necessary to undertake basic modeling and statistical decision techniques in engineering.

1. Understand and apply the basic concepts of probability theory and statistics to engineering problems.
2. Understand the relationship between random variables and their distribution functions.
3. Be able to construct confidence intervals on mean and variances
4. Be able to perform point estimation and statistical tests

1. Motivations, Data and Decisions, Uncertainties, Measurement and
2. Computational Errors (2 Hours)
3. Basic Probability, Sample Space, Events, Axioms of Probability, Sample Spaces with Equally Likely Outcomes, Conditional Probability, Independent Events (6)
4. Random Variables, Continuous/Discrete RV’s, Expectation, Variance, Covariance, Conditional Distributions, Moment Generating Functions, (10)
5. Special Distributions, Bernoulli, Binomial, Negative Binomial, Poisson, Uniform, Normal, Exponential, Chi-Square, T, F (9)
6. Sampling, Central Tendencies, Sample Variance, Empirical Distributions, Goodness of Fit, Sampling from Normal Populations (8)
7. Parameter Estimation, Moments Method, Maximum Likelihood, Interval Estimates (3)
8. Hypothesis Testing, Significance Levels, Tests Concerning Means, Tests Concerning Variances (3)

Class Requirements:

1. Three lecture sessions per week
2. Approximately 14 homework assignments per semester
3. Two examinations and a final
4. Some in class quizzes

Computer assignments may be given to run computational experiments illustrating the material covered in the course. If needed, the instructor will make freely available his software package MIST: A MATLAB Introductory Statistical Toolbox. Other software and simulation utilities found in the internet (WWW) will also be used.

Laboratory Projects: None

1. Class examinations.
2. Homework evaluation

1. Mathematics or Basic Science 0 credits

2. Engineering Science or design 3 credits

3. General Education requirements 0 credits

4. Major design experience 0 credits

Contribution to program objectives: Goals 1, 4

Prepared by: Emmanuel Fernandez   Date: April 14, 1998