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## ST515 - Experimental Statistics for Engineers I

- Prerequisites: Graduate standing. Students taking this course will require competence in differential and integral calculus (at the level of MA141 and MA 241).
- Term & Frequency: Fall and Spring
- Student Audience: Graduate students in engineering disciplines
- Credit: 3 credits
- Recent Texts: Hines, Montgomery, Goldsman and Borror (2003). Probability and Statistics for Engineering. John Wiley & sons, Inc..
- Recent Instructors: Soumendra Lahiri, Alyson Wilson, Daowen Zhang
- Background and Goals: This two-semester course sequence (ST515-6) covers the important aspects of probability and statistical techniques useful to engineers and researchers in other technical fields. The objective of the course is to develop an understanding and working knowledge of basic probability and statistics. The course provides an introduction to the rudiments of probability calculus, to discrete and continuous random variables and their probability distributions, and to sampling distributions. Concepts, methods and applications are emphasized, rather than theory. Successful completion of this course will provide a basic foundation for understanding probability and statisticly based material presented in other courses.
- Content: Event probability basics: experiments, outcomes, sample space, sample point, events, set algebra, probability, independence of events, conditional probability; One-dimensional discrete and continuous random variables: Probability distributions and density functions. Cumulative distribution functions, expectation; Functions of a random variable: distributions, expectations, and moment generating functions; Multi-dimensional random variables and their distributions; independence, expectation of functions of random variables, covariance, moments of linear functions; Families of discrete and continuous distribution. normal distribution; graphical and numerical descriptions; Random samples. Important sampling distributions; Statistical inference including parameter estimation and hypothesis testing; Analysis of Variance and Simple Linear Regression.
- Alternatives: ST 511 Experimental Statistics for the Biological Sciences I covers similar statistical methods, but with an emphasis on methods applicable to, and examples from, biological rather than engineering sciences.
- Subsequent Courses: ST 516 Experimental Statistics for Engineers II

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